Pesticide Aquatic Risk Indicators

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Pesticide Aquatic Risk Indicators - an examination of the OECD indicators REXTOX, ADSCOR and the Danish indicators FA and LI based on Danish sales data from 1992-2000 by Flemming Møhlenberg DHI, Water & Environment Kim Gustavson DHI, Water & Environment Peter B. Sørensen, National Environmental Research Institute Denmark 2 Index 1 Background...... 4 2 Introduction ...... 5 3 Description of Indicators ...... 6 3.1 Frequency of Application ...... 6 3.3 REXTOX...... 8 3.4 ADSCOR...... 8 4. Quantitative evaluation of pesticide indicators...... 9 4.1 Data sources...... 9 4.2 Principles of indicator calculation ...... 10 4.3 Temporal variation in Indicators...... 11 4.4 Pesticides dominating Indicators ...... 13 4.5 Quantification of input variability...... 15 5. Qualitative analysis...... 18 5.1 REXTOX...... 18 5.1.2 A simpler version of REXTOX ...... 19 5.1.3 Effect of buffer zones in reducing pesticide transport to streams ...... 21 5.2 ADSCOR...... 23 5.3 FA – Frequency of Application ...... 24 5.4 LI – Load Index ...... 26 6. Conclusion...... 27 Annex A. Sale of pesticides in Denmark...... 29 Annex B. Standard dosages of pesticides. Kg ha-1...... 31 Annex B. Standard dosages of pesticides. Kg ha-1...... 32 Annex C. Calculated Sprayed area (1000 ha) using standard dosage levels...... 33 Annex D No-spray zones (m) required for risk mitigation...... 35 Annex E. Data availability and gaps...... 37 Annex E. Data availability and gaps (continued) ...... 38 Annex F. Fate data DT50 (days)...... 39 Annex F. DT50 Continued...... 41 Annex G. Fate data Kd ...... 42 Annex F. Kd Continued...... 44 Annex H. Acute Toxicity. Fish96hrLC50 (mg l-1)...... 45 Annex I. Acute Toxicity. Daphnia 48hrEC50(mg l-1)...... 48 Annex J. Acute & Chronic Toxicity. Algae96hrEC50 ...... 50 Annex K. Chronic Toxicity. Fish21dayNOEC ...... 53 Annex L. Chronic Toxicity. Daphnia 21day NOEC...... 54 Annex L. Chronic Toxicity. Daphnia 21day NOEC...... 55 Annex M. Fraction of missing data for acute indicators...... 56 Annex N. Fraction of missing data for acute indicators...... 56 Annex O. Example of random selection of input data of acute fish toxicity...... 57 Annex P. Min - Max risk indices for fungicides...... 58 Annex Q. Min - Max risk indices for growth regulators...... 59 Annex R. Min - Max risk indices for herbicides...... 60 Annex S. Min - Max risk indices for insecticides...... 61 Annex T. Scaled risk indices for Insecticides & Herbicides...... 62 Annex U. Scaled risk indices for Fungicides & Growth regulators...... 63 Annex X. Indicator agreement on trends...... 64 Annex Y. Trend analysis for Indicators during the period 1992-2000...... 65 Annex Z. Exposure estimation of REXTOX in field studies...... 66 Annex AA. Effect of buffer zone width on spray drift and run-off ...... 67 Annex BB. Modelling relation between FA and Pesticide Effects on Aquatic life...... 68 Annex DD. Toxicity function in Indicators ...... 69 Annex EE. Calculation of FA...... 71 Annex FF: Calculation of LI...... 72

3 1 Background

As a respond to the recommendation, that OECD develop “systems to measure progress in risk reduction”, made by the OECD/FAO workshop on pesticide risk reduction in Sweden 1995, a workshop on pesticide risk indicators was held in Copenhagen in 1997.

The workshop recommended that the Pesticide Forum should undertake work to facilitate development of pesticide risk indicators. It was estimated that the indicators could help national governments to obtain baseline information about pesticide use and risk and then track risk trends over time.

The workshop did also recommend that it would be better to have a set of indicators dealing separately with risks to human health and to different compartments of the environment, rather than to have one single indicator of pesticide risk.

OECD Pesticide Forum did on that background initiated the Aquatic Risk Indicator Pilot Project in spring 1998 as a effort to help OECD countries to monitor risk trends over time and evaluate the effectiveness of risk reduction policies. In Phase 1 of the pilot project an Expert Group defined 3 indicators to be tested for consistency using real data of pesticide use in Phase 2. Two indicators (REXTOX and ADSCOR) were developed in 4 versions allowing to calculate acute or long-term risks indices associated with single crops (unscaled versions) and at a regional- or country wise level of aggregation taking account of pesticide use on single crops and the area contribution of various crops (scaled indicators). Acute and long-term risks could be calculated using corresponding acute or chronic input data for fate and toxicity. The third indicator SYSCOR was constructed in a scaled version, only.

Phase 2 included a thorough testing of the Indicators using data on actual pesticide use in arable and orchard crops in England and Wales for the period 1977 – 1996 (Report Phase 2).

This report constitutes the Danish contribution to Phase 4 of the Pilot Project on aquatic risk indicators. The Danish testing has included the OECD indicators REXTOX, ADSCOR and the Danish indicators FA (Frequency of Application) and LI (Load Index). The revised SYSCOR Indicator was however released too late to be included in the project. Denmark has focused on examining and comparing the temporal variation in the different indicators for the period 1992- 2000 as well as on the indicator sensitivity to variability in input data.

The indicators has been calculated using available information on sale of individual pesticides, area of different crops, buffer zones and recommended doses. The indicators were calculated separately for different groups of pesticides and for all pesticides, and risks calculated separately for algae, daphnia and fish. The sensitivity to input data variability was also quantified as part of an evaluation of the overall robustness of the indicators with respect to describing changes in time trends.

4 2 Introduction Pesticides are used in agriculture to control weed (herbicides), pests, including insects (insecticides), and plant diseases (fungicides). An other type of pesticide applied to fields in significant amounts includes growth regulators. Different groups of pesticides have different types of effects on aquatic organisms thus making generalisation rather difficult. Most pesticides are targeted to interfere with specific metabolic processes such as inhibition of electron transport, synthesis of aminoacids, lipid metabolism or mitochondrial respiration. Effects at the biochemical and cellular level are translated into various effects at the organismic level (cancer, immune and hormonal system) which reduce fitness of the individual and eventually cause mortality.

The ecological effects of pesticides are varied, inter-related and often act in concert with other stressors such as other contaminants, eutrophication and pathogens. In combination different stresses can be simple additive or effects can be synergistic. An important issue is that many of these effects are chronic and non-lethal, i.e. exposures may not result in immediate death but subtle effects may later reduce reproductive output affecting the long-term survival of the population. Other non-lethal effects include changes in behaviour, e.g. several insecticides induce drift in populations of insect larvae and crustaceans. Because of trophic interactions effects of pesticides usually extend beyond populations to ecosystems, e.g. reduction of submerged plant and algal biomass by herbicides may indirectly affect insect larvae and fish by reducing food availability and deterioration of habitats, and insecticides can indirectly affect fish populations by removing their food.

Even though our knowledge is incomplete there exist a wealth of information on how pesticides are transported from the field to surface waters, how they are distributed and degraded and how they affect aquatic life. However, detailed understanding and quantification of all processes invariably will narrow the applicability and make our predictions rather specific for particular sites. On the other hand to evaluate risk trends policy makers do need simple tools or indicators To develop easy to use indicators that extract the critical information of complex natural systems is a serious challenge that has previously been pursued by the European Union in the CAPER Project, various EPA workshops and extended by the OECD in the Aquatic Risk Indicator Pilot Project. It is the intent through different phases of development and validation to compare and improve different indicators, so they can be applied for policy use and analysis by governments and other stakeholders. Such indicators will eventually provide a series of key benchmarks to monitor changes in risks.

Given the complexity of natural systems and the diverse action of pesticides the limitations of simple indicators should be recognised. Indicators are built on information on direct effects, and calculated risks will not include any information related to interaction in the aquatic system. Indicators provide estimates of risk trends and not absolute measures of actual risks and indicators ultimately rely on the quality of input data. Hence, insufficient data on use, fate and effects will translate into poor or unreliable estimates of risk trends.

5 3 Description of Indicators The indicators evaluated include the two Danish indicators: Frequency of Application (FA) and Load Index (LI), (Clausen 1998) and the two OECD Indicators REXTOX and ADSCOR. The indicators differ in complexity and especially in the amount of input data needed to calculate indicator values with low data requirements by the Danish Indicators while the OECD indicators requiring much more input data (Table 3.1)

Table 3.1 Data requirements of indicators.

Parameter FA LI REXTOX ADSCOR Acute Chronic Acute Chronic Acute Chronic Total usage + + + + Sprayed area1 +++ Dosage ++ Spray zone length + + + + Run off zone + + + + Kd ++ + DT50 + + Fish96hrLC50 + + + Fish21dayNOEC + + + Daphnia48hrEC50 + + + Daphnia21dayNOEC50 + + + Algae96hrEC50 + + + Algae96hrNOEC + + + Total sale 1: Calculated as Sprayed area = Dosage

A description of the mathematical equations of the indicators REXTOX and ADSCOR have been described in detail in the Report of Phase 2 (ref ). In this report the general form of indicators are outlined in order to show similarities and dissimilarities between indicators.

3.1 Frequency of Application The Frequency of Application is the calculated average number of pesticide applications per year.

FA and LI was the key indicator of the first Danish Pesticide Action Plan from 1986 where the goals were a re-evaluation of all pesticides and a 50-percentage reduction of FA. (Status of the Minister for the Environment’s Action Plan for reducing the Consumption of Pesticides). FA constitutes the key indicator of Pesticide Action Plan II from 1999. The indicator Frequency of Application (FA) was developed in the mid-eighties because it was realised that the increasing use of low dose products was not reflected in the Danish statistics on sold amount of active ingredients. Thus a drop in sales of active ingredient can easily take place at the same time as the number of application - and pesticide load on the environment - increases. The indicator considers the quantities of each active ingredient sold, the standard dose of each active ingredient in each crop/crop type and the area of arable land in Denmark:

6    SAactiveingredient   SDcroptype  FA = ∑ , (3.1) all active ingredients AGRAyear where, SA denotes Sold amount of individual active ingredients per year SD denotes a defined standard dose for each individual active ingredients in each crop/crop type and AGRA is the area of arable land in Denmark.

More details on the calculation of FA can be found in annex EE The indicator is regarded as an indicator for the spraying intensity as well as an overall indicator of the environmental impact of pesticides. Because FA is based on a standard dose that relates to the biologically active field dose it is assumed to reflect the direct effect on target organisms as well as the indirect impact on ecosystems, which results from changes in the quantities and species found in the food chain. In chapter 5 it is discussed to what extend the standard field dose varies inversely with toxicity to non-target organisms as fish, daphnia’s and algae. In addition, an example is presented on predicted changes in effects on aquatic life following 4 different pesticide reduction scenarios.

3.2 Load IndexThe Load Index (LI) is the calculated number of toxic doses in the sold amount of pesticides.

The indicator Load Index (LI) has been used to track changes in potential pesticide impact on environment and health as a result of the Danish re-evaluation of pesticides and the plan to reduce the use of pesticides. The indicator calculates the ratio between Total sale of different pesticides:Toxicity summed for all active ingredients to follow if number of toxic doses has changes as a result of either changes in sales and/or toxicity

Saleseachactiveingridient LI = ∑ , (3.2) ⋅ all active ingredients TOX AGRAyear where TOX represents acute or long-term LC50 or LD50values.

More details on the calculation of LI can be found in annex FF

The indicator is calculated separately for mammals, birds, earthworms, fish, crustaceans and algae using a value (average, min or max) for toxicity of individual pesticides. The calculated values are designated "load indices for mammals", "load indices for fish", etc. The LI provides a relative measure of environmental load concerning specific type of toxicity. In line with the OECD indicators LI is not a measure of actual effects on populations or ecosystems in the field but calculates a relative risk that can be compared between years. LI does not include information on exposure risks or buffer zones required for risk mitigation. Such buffer zones can easily be implemented though by imposing scaled reductions on total sales of pesticide according to buffer zone widths.

7 3.3 REXTOX REXTOX is a mechanistic OECD indicator based on national indicators developed in the Netherlands and Germany. Among the 4 indicators tested REXTOX is the only indicator that calculates the amounts of pesticides that are likely to end up in surface waters due to spray drift and surface run-off, while drainage is ignored. The calculated pesticide exposure estimate is divided by toxicity to obtain a risk index aquatic organisms.

= Total usage ⋅ + REXTOX ∑ (L%spraydrift L%runoff ) , (3.3) all active ingredients TOX where L%spraydrift is the percentage of the applied amount entering the surface water by spray drift and L%runoff is the percentage of the applied amount entering the surface water by run off.

Calculation of exposure (L%spraydrift + L%runoff) is very detailed even in the acute version as it takes account of slope, precipitation, soil type, width of buffer zones required for risk mitigation and pesticide characteristics (Kd and half-life) to arrive at pesticide concentrations in waters (Report Phase 2). In testing REXTOX with Danish data default value of slope, C content in soil, water index and precipitation were used throughout in the calculation of pesticide loss in REXTOX. In chapter 5 several assumptions of the exposure calculations of REXTOX are critically discussed. In addition, accepting the relative importance of spray drift and surface run- off as calculated by REXTOX a simpler expression of exposure is derived.

3.4 ADSCOR ADSCOR is a hybrid indicator that uses additive scores for exposure (Dosage, Spray (buffer) zones, frequency of treatment, Application Method) that subsequently is multiplied by the ratio Total area sprayed:Toxicity to calculate a risk estimate for aquatic organisms (Report Phase 2):

Sprayed area  ADSCOR = ∑∑ ⋅ Dosage,Spray zone, Runoff zone, ApplM  (3.4) allactive ingredients  TOX scoring 

Using the Danish data frequency of treatments per season of individual pesticides could only be calculated from 1997 onwards, hence the scoring for this variable was set to 0 throughout the period in the ADSCOR calculations. In chapter 5 several issues of ADSCOR such as a low resolution in several scores is discussed in the context of currently used pesticides in Denmark.

Both REXTOX and ADSCOR exist in unscaled and scaled versions. Unscaled versions calculate the risks associated with pesticides applied to a single field and subsequently average risks to obtain an overall measure. The scaled version provide a measure of total risk taking account of both local severity of risk and the area treated with each pesticide. The present examination will focus on the scaled versions only, because they are more related to the impact measure calculated by FA and LI than the unscaled versions and because the most important use of the indicators will be to indicate the countrywide environmental impact.

8 4. Quantitative evaluation of pesticide indicators

4.1 Data sources Actual data of pesticide use on Danish arable land is currently not available. Therefore, use data was calculated from sales data, area of different crops and recommended dosage of active ingredients (Annex A-C). For pesticide products with more than one active ingredient the area treated was calculated based on the main ingredient. Laboratory data on toxicity and fate of pesticides were extracted from a database established by the Danish EPA in connection with the pesticide approval procedure and supplemented when necessary with data from US EPA database “Acquire” and data collections carried out by Clausen (1998) and Hansen (2000). Data/results presented as larger than or smaller than a given value are not included in calculation of the indicators.

100 100

80 80

60 60

40 Missing 40 Avail. 20 20 Dist.fate of data- % 0 0 Dist. of acute tox data - % % - data tox acute of Dist. Kd DT50 Fish Daphnia Algae

100 Figure 4.1. Availability of data for fate 80 parameters (Kd and DT50) and toxicity of 60 pesticides to Fish, Daphnia and Algae. Average values of arable area represented 40 calculated for the period 1992-2000. Total 20 number of pesticides =106. 0 Dist. chronicof tox data %- Fish Daphnia Algae

Table 4.1. Data gaps Variable Number of substances with Percentage of the total number data gaps (106) DT50 6 6 Kd 12 11 Acute Fish 9 8 Acute Daphnia 13 12 Acute Algae 15 14 Chronic Fish 83 78 Chronic Daphnia 68 65 Chronic Algae 80 76 9 Data availability for acute toxicity and fate of pesticides was almost complete with more than 95 % of the arable area represented (Fig. 4.1). On the other hand, data for chronic toxicity was far from complete especially for long-term toxicity to fish and daphnia with more than half of the arable area not represented. Such large fraction not accounted for is likely to produce unreliable results and accordingly chronic indicators were not included in this test. A detailed enumeration of data availability distributed among years and indicators is shown in Annex E.

4.2 Principles of indicator calculation The usage of pesticides was calculated from sales data, recommended dose levels and using the yearly statistics on area coverage of different crops. Hence, for each pesticide two numbers characterise the area treated and amount dosed on this area. In contrast, for every parameter describing the fate and toxicity to fish, daphnia and algae several alternative values exist. The specific choice of property values will influence the indicator value and this influence needs to be quantified in order to evaluate the indicators. The effect of varying parameter values on Risk Indicator values and calculated time trends is discussed in section 4.5.

In the quantitative test of indicators four different selection approaches were used: 1) averaging all available values of each input parameter, 2) selecting the maximum value of toxicity, Kd and minimum value of LD50 to produce the lowest indicator value, 3) selecting the minimum value of toxicity, Kd and the maximum value of LD50 to produce the highest indicator value and 4) random selection of sets of parameter values, but using these sets throughout the whole period. An example of the random selection procedure for a single pesticide is shown in Annex O. By combining results from random selections for all pesticides the variation in risk indices can be calculated (Fig. 4.2 ).

25 Fish all pesticides

LI value LI 10

0 1992 1993 1994 1995 1996 1997 1998 1999 2000 Figure 4.2. Fifty alternative time series of LI values for fish (thin lines ) and the maximal and minimal possible value time series as (thick lines)

Despite an almost 5 fold variation in absolute values of LI the time trend seemingly is rather consistent irrespective of set of parameter values chosen. Therefore, a large variability as shown in Fig. 4.2 does not necessary mean a high uncertainty in the ability to make time trend predictions. This is further discussed in section 4.5.

10 4.3 Temporal variation in Indicators In the time trend analysis indicator values were calculated for the period 1992-2000 using data for pesticide sale, area of different crops, dosage of pesticide, width of buffer zone (that was changed for several insecticides during the period) and averaged data for toxicity and fate. Indicator values and trends were calculated separately for fish, daphnia and algae both for all pesticides and for each pesticide group. Figure 4.3 shows the temporal variation in indicators calculated for all pesticides. Temporal variation in indicators calculated for insecticides, fungicides, herbicides and growth regulators are shown in Annex P - U.

2.5 Fish FT 6 Algae LI 5 2.0 REXTOX REXTOX - buffer 4 1.5 ADSCOR 3 1.0 2 0.5 1 0 0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 Figure 4.3. Temporal variation of risk 8 Daphnia 7 indicators calculated for all pesticides. 6 Calculations were based on averaged input 5 4 values. Two versions of REXTOX are 3 shown – with and without no spray buffer 2 1 zones required for risk mitigation. 0 Indicator values are scaled to a time mean 1992 1994 1996 1998 2000value of unity for each specific indicator.

Among the 4 indicators REXTOX generally showed the strongest temporal variation followed by ADSCOR and FA or LI. With one exception (LI algae) risk indicators calculated for all pesticides showed significant decreasing trends for fish, daphnia and algae when the whole period was included (Fig. 4.3 and Table 4.1; temporal trends calculated separately for pesticide groups are shown in Annex T & U). Most indicators showed a peak in 1995 followed by a minimum in 1996 due to stockpiling in anticipation of a substantial increase of the rate of tax on pesticides in 1996. Such biases are to be expected when pesticide use is derived from sales data. The pattern with local minima and maxima overriding a monotonous trend illustrate that temporal trends must be calculated based on several years information otherwise erratic conclusions may result. For the most responsive indicator REXTOX 4-5 years’ data was needed to obtain a significant trend, while 9 years’ data was required to obtain a significant trend for the indicator FA using raw sales data for individual years (Fig. 4.4). However, the average value for sales in 1995 and 1996 is properly a reasonable estimate for use in 1995 and 1996. Using the average value for indicators 1995-95 significant trend is obtained using 5 years’ of data. Alternatively, using a 3 year running average a significant trend in FA is obtained using 4 years’ data only (Fig. 4.4).

11 Table 4.2. Trend analysis for Indicators during the period 1992-2000. Calculated indicators for the different years were log transformed causing indicators to be normally distributed. Residuals were then calculated by subtracting from each year’s value the corresponding average value for the period 1992-2000. Residuals were subsequently tested for temporal trends using Kendall’s τ. Indicator Kendall’s τ Probability Fish FA -0.556 0.0371 LI -0.500 0.0606 REXTOX -0.722 0.0067 ADSCOR -0.500 0.0218 Daphnia FA -0.556 0.0371 LI -0.556 0.0371 REXTOX -0.778 0.0035 ADSCOR -0.611 0.0218 Algae FA -0.556 0.0371 LI 0.000 1 REXTOX -0.833 0.0018 ADSCOR -0.778 0.0035

0.14 1.2 REXTOX - Fish FA 0.12 1.0 0.10 0.8 0.08 0.6 0.06 0.4

p-value of trend of p-value 0.04 p-value of trend of p-value 0.02 0.2 0.00 0.0 3 4 5 6 7 8 9 3 4 5 6 7 8 9 Number of consecutive years Number of consecutive years 0.7 0.14 0.6 FA (95&96 avr.) 0.12 FA (3 yr. avr.) 0.5 0.10 0.4 0.08 0.3 0.06 0.2 0.04 p-value of trend of p-value 0.1 trend of p-value 0.02 0 0 3 4 5 6 7 8 9 3 4 5 6 7 8 9 Number of consecutive years Number of consecutive years

Figure 4.4. P-value of temporal trend of indicators REXTOX-Fish and FA as a function of number of consecutive years included in analysis. Trend of FA calculated using raw sales data (FA), data averaged for 1995-1996 (95&96) and using a running 3 year average (3 yr. Avr.). Stipulated line indicates p-level of 0.05. Data analysed from year 2000 and back in time.

The agreement between indicators was evaluated by comparing the direction of change (upward “+”or downward “-“) through progressing pair of years (1992,1993), (1993,1994), etc. The results for indicators calculated for all pesticides are shown in Table 4.2, where the numbers of “+” and “-“ are summed in the columns at right. For several years the agreement between 12 Indicators was good (e.g. 1994-1995, 1997-1998), however ADSCOR indicator calculated for algae was quite often in conflict with the other indicators. The analysis is further detailed in Annex X.

Table 4.3 Direction of change in indicator values (calculated for all pesticides) between progressing pair of years. + = values increasing (+); - = values decreasing. Two versions of REXTOX are included, i.e. taking account and not taking (excl. zone) account of buffer zones required for risk mitigation.

FA LI REXTOX REXTOX ADSCOR Sum Year excl. zone Fish Daphn Algae Fish Daphn Algae Fish Daphn Algae Fish Daphn Algae +-

1992 1993 - + + - - - - + -----310 1993 1994 - - - + - - - - - + - + - 3 10 1994 1995 + + + + + + + + ++++ -121 1995 1996 ------+112 1996 1997 + + + + + + - + ++++ -112 1997 1998 ------013 1998 1999 + + + - - - - + + - - + - 6 7 1999 2000 ------013

4.4 Pesticides dominating Indicators Pyretroid insecticides (esfenvalerate, lambda-cyhalothrin, alpha-cypermethrin, cypermethrin) were the most influential pesticides for fish and daphnia indicator values and accordingly specific indicators calculated for insecticides showed almost identical trends as indicators calculated for all pesticides (see Annex T). However, also the herbicide pendimethalin and the fungicides mancozeb and azoxystrobin were among the top 5 pesticides in several indicators (Table 4.2)

13 Table 4.4. Pesticides dominating Indicator values calculated for Fish, Daphnia and Algae. N = number of years (within the period 1992-2000) a specific pesticide was among the most important 5 pesticides contributing to Indicator value. Letters in brackets I: insecticide; H: herbicide; F: fungicide.

LI REXTOX ADSCOR Fish Pesticide N Pesticide N Pesticide N Esfenvalerate (I) 9 Esfenvalerate (I) 8 alpha-cypermethrin (I) 9 lambda-cyhalothrin (I) 9 lambda-cyhalothrin (I) 8 Esfenvalerate (I) 9 Cypermethrin (I) 7 alpha-cypermethrin (I) 7 lambda-cyhalothrin (I) 9 alpha-cypermethrin (I) 5 Cypermethrin (I) 5 Cypermethrin (I) 7 Pendimethalin (H) 4 delta-methrin (I) 5 delta-methrin (I) 6 Bromoxynil (H) 3 Mancozeb (F) 4 Tau-fluvalinate (I) 4 delta-methrin (I) 3 Azoxystrobin (F) 3 Bromoxynil (H) 1 Triflusulfuron methyl (H) 3 Tau-fluvalinate (I) 2 Tau-fluvalinate (I) 2 Bromoxynil (H) 1 Chlorothalonil (F) 1 Pendimethalin (H) 1 Daphnia alpha-cypermethrin (I) 9 Esfenvalerate (I) 9 alpha-cypermethrin (I) 9 Esfenvalerate (I) 9 alpha-cypermethrin (I) 8 Esfenvalerate (I) 9 Pendimethalin (H) 8 Cypermethrin (I) 7 Cypermethrin (I) 7 Cypermethrin (I) 7 Pendimethalin (H) 6 Pirimicarb (I) 6 Malathion (I) 4 Azoxystrobin (F) 3 delta-methrin (I) 5 Chlorfenvinfor (I) 3 Pyrazophos (F) 3 Pendimethalin (H) 5 Pyrazophos (F) 3 Linuron (H) 2 Azoxystrobin (F) 2 Azoxystrobin (F) 1 Malathion (I) 2 Chlorfenvinfos (I) 2 Pirimicarb (I) 1 Pirimicarb (I) 2 Chlorothalonil (F) 1 Glyphosat (H) 1 Maneb (F) 1 Algae Isoproturon (H) 8 Mancozeb (F) 9 Pendimethalin (H) 9 Mancozeb (F) 8 Diquat (H) 8 Isoproturon (H) 8 Propiconazol (F) 8 Isoproturon (H) 8 Propiconazol (F) 7 Pendimethalin (H) 7 Propiconazol (F) 5 Diquat (H) 6 Diquat (H) 6 Azoxystrobin (F) 3 Mancozeb (F) 5 Cyanazin (H) 3 Cyanazin (H) 3 Azoxystrobin (F) 3 Terbuthylazine (H) 2 Metamitron (H) 3 Cyanazin (H) 3 Azoxystrobin (F) 1 Metribuzin (H) 2 Fenpropidin (F) 2 Fenpropidin (F) 1 Aclonifen (H) 1 Metribuzin (H) 2 Prosulfocarb (H) 1 Fenpropidin (F) 1 Pendimethalin (H) 1 Terbuthylazine (H) 1

14 3.0 1.6 Herbicides Insecticides 1.4 2.5 1.2 1.0 2.0 0.8 FT 1.5 0.6 LI 1.0 0.4 REXTOX 0.2 ADSCOR 0.5 0 0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000

2.5 Fungicides 2.0 Figure 4.5. Scaled risk indices for Algae

1.5 calculated for herbicides, fungicides and

1.0 insecticides. Calculations were based on averaged input values. Indices have been 0.5 scaled to allow comparisons within 0 1992 1994 1996 1998 2000 indices but not between different indices.

Growth regulators, though not contributing significantly to the overall indicator values showed decreasing trends (but not significant) for indicators calculated for fish and daphnia, but not for algae (Annex U).

4.5 Quantification of input variability Absolute values of REXTOX, ADSCOR and LI indicators were very sensitive to variability in input values. Hence, by consequently selecting minimum or maximum values of toxicity, physico-chemical properties and fate, indicators varied between 1 and 5 orders of magnitude, but what was more important: the temporal trends were almost identical irrespective of min or max input values were used. The large variation due to the input variability is illustrated in the Figures 4.6-4.8 (and Annex P – S). A similar robustness of indicators to quantify trends was also demonstrated, if input values were randomly selected (but selected data sets used throughout the 1992-2000 period).

5 5 REXTOX Fish LI Fish 4 4 Min 3 3 Max

2 2

1 1

0 0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 5 ADSCOR Fish 4 Figure 4.6. Scaled risk indices for Fish 3 calculated for all pesticides using the minimum and maximum possible input 2 values for toxicity and fate. Notice that 1 indices have been scaled to allow 0 comparisons within indices but not 1992 1994 1996 1998 2000 between different indices. 15 6 5 REXTOX Daphnia LI Daphnia Min 5 4 Max 4 3 3 2 2 1 1 0 0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 6 ADSCOR Daphnia 5 Figure 4.7. Scaled risk indices for 4 Daphnia calculated for all pesticides 3 using the minimum and maximum 2 possible input values for toxicity and 1 fate. Notice that indices have been scaled to allow comparisons within 0 1992 1994 1996 1998 2000 indices but not between different indices.

4 3 REXTOX algae LI Algae min 3 max 2 2 1 1

0 0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000

ADSCOR Algae 4 Figure 4.8. Scaled risk indices for Algae 3 calculated for all pesticides using the minimum and maximum possible input 2 values for toxicity and fate. Indices have 1 been scaled to allow comparisons within indices but not between different indices. 0 1992 1994 1996 1998 2000

Despite such extreme test conditions the trends calculated with min or max input values with few exceptions were similar to temporal trends of indicators calculated with averaged input values (Figs 4.6-4.8). This analysis is further detailed in Table 4.4. Using either min or max input values REXTOX disagreed in one instance only (1994-1995 Fish). ADSCOR disagreed in 3 and LI in 4 instances. Therefore the indicators must be regarded as rather robust to demonstrate time trends.

16 Table 4.5. Analysis of indicator robustness to predict consistent trends between progressing pair of years using min or max values of fate and toxicity. A “+” denotes that both min and max values of input data result in same direction of change between years (i.e. no indicator uncertainty due to input uncertainty), while “-“ sign denotes opposite sign of change using min or max input values (i.e. a possible uncertainty in indicator prediction).

LI REXTOX ADSCOR Years Fish Daphnia Algae Fish Daphnia Algae Fish Daphnia Algae 1992 1993 - + - + + + + + + 1993 1994 + + - + + + + - + 1994 1995 + + + - + + + + + 1995 1996 + + + + + + + + - 1996 1997 + + + + + + + + - 1997 1998 + + + + + + + + + 1998 1999 + - + + + + + + + 1999 2000 + + + + + + + + +

Changes in availability of input data may bias temporal trends. New pesticides will usually be backed up by fewer data for toxicity and fate than “old pesticides” introduced earlier in the period analysed. If so, variability in risk indicator values invariably will increase towards the end of the period examined. To illustrate this, a random sampling of 2, 5 and 10 LC50 values out of a total of 18 LC50 values for toxicity of α-cypermethrin to fish was carried out (Fig. 4.9). If only two set were sampled the average LC50 varied between 0.0008 and 0.181 mg l-1. The range decreased with increasing number of data sets sampled (5 sets: 0.0017 to 0.126; 10 sets: 0.054 to 0.098 mg l-1). Assuming the average LC50 of 18 individual values (0.057 mg l-1) represent the “correct” average value of toxicity under-sampling (e.g. representing pesticides with 2 data available) may result in gross under- and overestimates of the “true” average toxicity value (0.3 and 71 times). At increasing availability of toxicity data the average value approaches the “correct” average value (Fig. 4.9). However, as trends were rather consistent across indicators the bias probably was more theoretical than real.

Figure 4.9. Range in average ) -1 1.0000 EC50 values for Fish exposed to α- cypermethrin. Out of a total of 18 0.1000 individual EC50 values 2. 5 and 10 and 18 (all) values were randomly 0.0100 sampled and averaged. The sampling was carried out 20 times 0.0010 resulting in a range of averaged EC50. Single species data were 0.0001 Average EC50 value (mg l (mg value EC50 Average obtained from the Danish EPA 0 5 10 15 20 data base and Aquire (see Annex Number of data sets sampled out of 18 H).

17 5. Qualitative analysis

This chapter focuses on the suite assumptions behind the different indicators. REXTOX as the most complex, detailed and ‘data-demanding’ indicator naturally will receive the most attention especially what concerns the calculations of exposure. Further, selected breakpoints of the ADSCOR are discussed and an example of the applicability of the Danish indicator FA is presented.

The indicators differ primarily in how exposure risk is calculated, while the toxic effects more or less uniformly relate to the pesticide dose/exposure divided by some measure of toxicity. For that reason the toxic equations of indicators are discussed separately.

5.1 REXTOX The basic form of the scaled REXTOX indicator is: ⋅ = ADR CAT ⋅ + REXTOX ∑ (L%spraydrift L%runoff ) (5.1) all active ingredients TOX where ADR (kg/ha) is the dose rate, CAT the accumulated sprayed area (ha), TOX is the measure for toxicity, L%spraydrift the percentage of the applied amount entering the surface water by spray drift and Lrunoff is the percentage of the applied amount entering the surface water by run off.

5.1.1 Exposure calculation Spray drift, edge-of-field runoff, subsurface transport through transient inter-flow in the normal unsaturated zone and drainage constitute the major routes of nonpoint-source pesticide input into aquatic surface waters. In Europe current regulatory risk assessment focus largely on spray drift and this priority is also reflected in the REXTOX indicator. This is in contrast to reported serious incidents of fish kills in streams and rivers associated with non-point pesticide sources. They all relate to surface run-off events in connection with heavy rainfall and not to spray drift during ground pesticide application. Therefore, focussing on spray drift the effect of buffer zones required for risk mitigation to protect aquatic life probably will be underestimated as the effect of buffer zones probably is more important for run-off than for spray drift.

REXTOX considers spray drift and surface run-off only. Spray drift is calculated using derivatives of the Ganzelmeier Tables (Report of Phase 2; 12.1.2.1). In Annex Y is shown a comparison of measured concentration of pesticides as a result of spray drift and surface run-off (and drainage) and calculated concentrations according to the indicator REXTOX. The information has been collected from various literature sources that contain sufficient information on environmental characteristics, mode and intensity of application to satisfy the input needs of REXTOX. In some studies information on one or two environmental characteristics was missing. In these cases default values of REXTOX was assumed (see Annex Y).

As shown in Fig 5.1 REXTOX generally underestimate concentrations in streams resulting from run-off of pesticides, while spray drift is estimated correctly.

18 100

-1 Figure 5.1 Comparison of pesticide 10 concentrations estimated by 1 REXTOX and actual concentrations measured in streams. Data from 0.1 Annex BB. Run-off 0.01 Measured - µg l - Measured Spray drift 0.001 0.001 0.01 0.1 1 10 100 REXTOX - µg l-1

Pesticides in surface run-off are either in soluble form or associated with particles. Due to higher mobility (i.e. low Kd) and thus propensity to loss by surface run-off most studies have considered herbicides, while studies on insecticides with high particle affinities are few, because these pesticides are not expected to run-off in significant amounts and if so then primarily associated with particles. These considerations are also reflected in REXTOX. But insecticides do enter streams by run-off, they appear in the water phase and even though not in true solution they are as toxic as expected if in solution (Baughman et al. 1989, Mathiessen et al. 1995, Schulz & Liess 1999a, Schulz & Liess 1999b).

The inherent dominance of spray drift over surface run-off in REXTOX is further illustrated below. The values of L%spraydrift and L%runoff were calculated for all pesticides using the non- spray zone for year 2000. The year 2000 represents the most restrictive (widest) spray zone for the most pesticides (see Annex D). Hence, this analysis favours for the run-off zone influence as the spray drift is at a minimum value. The L%spraydrift values differs only between substances as a result of different non spray zones, so the input uncertainty as due to varying fate values (Kd and DT50) will not influence L%spraydrift. On the contrary, Lrunoff is affected by the uncertainty related to the fate parameters Kd and DT50, will create uncertainty in the having Kd as the most important parameter to induce uncertainty. In order to evaluate the dominance of the L%spraydrift the maximum possible influence from L%runoff compared to L%spraydrift is evaluated simply by using the maximum possible estimates of the L%runoff values based on the databases yielding the following result:

∑ L% runoff all active ingredients = 0.13 (5.2) ∑ MAX (L% spraydrift ) all active ingredients

This clearly indicates that the Spray drift is assumed to be the dominating route of entry to the surface waters in REXTOX.

5.1.2 A simpler version of REXTOX If the run-off is neglected and the spray drift is assumed to be dominating or in other words if the REXTOX indicator is assumed “valid” in the present form using the present data, then it is possible to derive a simpler version of the indicator. 19 The equation for L%spraydrift in early season (i.e. herbicides) is

b L% = (a + )2 (5.3) spraydrift 1+ x where a and b are empirical coefficients and x the spray zone width. The spray zone width is added by 1 in order not to make infinite vales for x = 0 (this of cause will decrease the actual estimates of spray-drift). The loss of other pesticides applied later in season (with vegetated buffer zone) is described by the equation:

= a−b⋅ln(x+1) L%spraydrift e (5.4) where the x is added by 1 as in Eq. 5.3.

Only 3 spray zones (0, 10 and 20m, see Annex D) are used in the data set so the pesticide input to a stream can attain the following 3 values (% of application):

Table 5.2. The percentage of applied amount, which enters the surface by spray drift according to REXTOX. Buffer Zone Herbicides Others Average width 0 (+1) 4.08 4.95 4.5 10 0.22 0.28 20 0.16 0.13  0.2

The results in Table 5.2 shows that the spray buffer zone do have a dramatic influence on the spray drift. Thus the exposure due to spray drift calculated by REXTOX roughly splits the pesticides into two groups: those with no buffer (spray) zone and those with buffer zone. In this way it is possible to write an approximate version of Eq. 5.1 using the Table 5.2 coefficients as:

ADR ⋅CAT ADR ⋅CAT REXTOX ≈ 4.5⋅ ∑ + 0.2 ⋅ ∑ (5.5) all not having spray zone TOX all having spray zone TOX

Therefore, the RETOX indicator splits up in two sums of tox-weighted usage very much similar to the Load index (LI):

ADR ⋅ CAT LI = ∑ (5.6) ⋅ all active ingredients TOX AGRAyear where AGRAyear is the total agricultural area for the specific year.

Combining Eqs. 5.5 and 5.6 yields:

≈ ⋅ ⋅ + ⋅ REXTOX AGRA (4.5 LI having no spray zone 0.2 LI having spray zones ) (5.7)

20 The conclusion of this analysis is that RETOX in reality represents a weighed sum of the simple LI between the two categories of pesticides: those with a spray buffer zone and those without spray buffer zone. Thus the simple version of REXTOX in form of Eq. 5.7 can replace the original version (Eq. 2.3) for the data used.

This can also be illustrated by a correlation analysis, where the REXTOX and LI are linearly correlated as shown in Fig. 5.2.

1,4E+07

1,2E+07 with bufferzone With out bufferzone 1,0E+07

8,0E+06

6,0E+06 REXTOX

4,0E+06

2,0E+06

0,0E+00 01234 Load index

Figure 5.2. Correlation between REXTOX and LI.

5.1.3 Effect of buffer zones in reducing pesticide transport to streams Buffer zones along streams, lakes and ponds can reduce the input of pollutants entering the adjacent water body either by intercepting spray drift or surface run-off. Buffer zones and especially if densely vegetated may act as a physical barrier for suspended solids and associated contaminants. Numerous studies have shown a huge variability in the effectiveness of the buffers, because of different width of buffers, ratio of crop field to buffer area, type of vegetation in buffer zone, soil texture and slope of field and buffer zone. In Figure 5.3 is shown a comparison of measured reductions of pesticides through buffer zones of various widths along with calculations carried out according to REXTOX. The reported values on environmental characteristics and information on pesticide data (Kd, DT50, see Annex F & G) has been used to calculate pesticide entry into streams with and without buffer zones.

Generally, the reduction of pesticide run-off through buffer zone is adequately calculated using REXTOX when buffer width is large, but REXTOX seem to underestimate the effect of narrow buffer zones (Fig. 5.3).

21 120

100 Fig. 5.3 Effect of buffer zone width on run-off (% reduction through 80 buffer zone) to streams. Calculated reductions according to REXTOX 60 and actual reductions measured 40 through buffer zones shown (See REXTOX Annex AA for references). 20 OBSERVED Reduction of pest conc (%) conc pest of Reduction 0 0 5 10 15 20 25 Buffer width (m)

In conclusion: • the role of spray drift in the REXTOX indicator invariably dominate over run-off • accepting a major role of spray drift a simpler version of REXTOX can be calculated • based on a brief literature survey the role of run-off in the REXTOX indicator seemingly is too small especially for insecticides with high particle affinities • the role of buffer zone width (especially if the zone is vegetated) is underestimated as both spray drift and run-off of pesticides show greater reduction than estimated by the REXTOX indicator.

22 5.2 ADSCOR

The basic form of the scaled ADSCOR indicator is:

Sprayed area  ADSCOR = ∑∑ ⋅ Dosage,Spray zone, Runoff zone, ApplM  (5.8) allactive ingredients  TOX scoring  where Sprayed area is the area treated, TOX is the measure for toxicity, Dosage is the score for Dose Rate, Spray zone is the score for Spray drift Buffer zone, Run-off zone is the score for Run-off Buffer zone and ApplM is the score for Method of Application. All input data except Sprayed area and TOX (-icity) are entered as scores.

5.2.1 Exposure calculation In the equation for ADSCOR the exposure is included in a scoring system (see Table 5.3).

Table 5.3. The scoring system used in ADSCOR to calculate exposure risk.

Parameter Single value Minimum value Maximum value Score 0.1 0 0.1 1 1 Average dose rate Kg/ha 132 3103 10 4 Method of application Seed treatment 0 Granular incorporated 1 Soil sterilant 1 Granular broadcast 2 Ground spray 3 Air blast 4 Spray drift buffer zone For ground spray -1 Runoff buffer zone -1

The average dose rate and method of application encompass the largest range in scores from 0 to 4, while presence or absence of buffer zones required for risk mitigation shows the lowest range (0 or –1). ADSCOR in the default form does not discriminate between different buffer widths.

Common to most scoring systems ADSCOR may not be applicable to all countries without resetting breakpoints and scores. If not ADSCOR may be too insensitive, e.g. 60% of the pesticides currently used in Denmark will only obtain score 0 or 1, and less than 2% will obtain the maximum score value on 4 for Average dose rate.

The influence of the different exposure scores on the indicator values was evaluated by calculating the contribution of the different exposure variables to the overall exposure score for the period 1992-2000 (see Table 5.4). A noticeable changes within this period was a more extended requirement for no spray buffer zones for several pesticides in 1997 (see Annex D).

23 Table 5.4. The total exposure scoring in ADSCOR for each year during the period 1992-2000.

1992 1993 1994 1995 1996 1997 1998 1999 2000 Average dose rate 103 105 103 70 72 73 76 88 71 Method of application 318 318 318 318 318 318 318 318 318 Spray drift buffer zone -6 -13 -17 -22 -27 -35 -39 -46 -47 Runoff buffer zone0000000-106-106 Total: 415 410 404 366 363 356 355 254 236

Sprayed area Overall ADSCOR is driven by the ratio , however, the temporal variation in the Tox total scoring as well as the contribution of the individual exposure variables highlights the influence from the spray and run-off zone (Table 5.4). Generally, buffer zones do influence the contribution from run-off to a much higher extent than contribution from spray drift, which seem to be consistent with literature values (see Annex Z & AA). This is in contrast to REXTOX, where the spray drift was dominating.

Conclusion: Like other scoring indicators ADSCOR offer several strengths including • easy of use and easy to adapt to specific environmental or management needs • scores allow to include all routes of exposure without calculating precise levels for each variable

However, ADSCOR (and other scoring systems) • inherently suffers from low sensitivity to react on even large changes in inputs because of few breakpoints for important variables • is rather subjective in determining break points and coefficients, making it difficult to make a general and objective evaluation of the scoring results in relation to other indicators such as REXTOX which is based on a deterministic approach

5.3 FA – Frequency of Application Among the indicators evaluated FA is by far the simplest as it does not include explicit estimates for exposure or toxicity.    SAindividualactiveingredients   SDcrop / croptypes  FA = ∑ , where all active ingredients AGRAyear

SA is the amount sold of individual active ingredients per year, SD the standard dose for each ingredient in each crop/crop type and AGRA the area of arable land in Denmark

Because FA is based on a standard dose that relates to the biologically active field dose it is implicitly assumed to reflect the direct effect on target organisms as well as the indirect impact on ecosystems, which results from changes in the quantities and species found in the food chain. This assumption probably is valid for target organisms in the field, but FA has also been shown to be a valid indicator for several bird species other terrestrial non-target organisms. Briefly, the populations of several bird species have been shown to correlate negatively with the number of 24 herbicide-, insecticide- and fungicide applications, i.e. FA (Pest Res. No. 34). In another study the preliminary results indicates that diversity of flora and fauna was increased following reductions in FA by 50% and 75% (Esbjerg et al. to be publicised in 2002). In addition, a modelling exercise demonstrated a close correlation between FA and the probability of significant reductions in populations of daphnia and algae in model ponds (Fig. 5.4, Annex BB). However, an examination on the most frequently used pesticides in Denmark indicates that an inverse relationship between applied dose and toxicity of pesticides may not be universally warranted for all non target aquatic organisms. It should be noted that the examination includes relatively few pesticides and that this may have biased the outcome significantly. (Annex CC).

100%

80%

60%

40% populations 20% Probability ofProbability detecting significant reductions in 0% 00.511.522.5 Frequency of application

Figure 5.4. Relation between probability for significant effects on populations of algae (♦) and daphnia (■) and FA index (from Møhlenberg & Gustavson 1998).

Conclusion: FA only considers sales of pesticides and the sprayed area. Therefor FA is the easiest to use indicator and by not relying on data on toxicity and fate FA inherently is objective (e.g. no choices how to select input data have to be made) and the most conservative among indicators tested. • FA produce risk trends very similar to those produced by more complex and data-intensive indicators like REXTOX (albeit less responsive and requiring longer periods to obtain significant trends). • Several studies (experimental and modelling exercises) have indicated that FA is a reasonable risk indicator for terrestrial and aquatic ecosystems.

However, • By not including no spray buffer zones required for risk mitigation FA will not be able to predict temporal variations in risks associated with adoption of new buffer zone widths, and • There are indications that suggest that an inverse relationship between applied dose and toxicity of pesticides may not be universally warranted for all non-target organisms in the aquatic environment. It should however be noted that the examination includes relatively few pesticides and that this may have biased the outcome significantly.

25 5.4 LI – Load Index Load Index is used as a supplement to FA in Denmark and does take account of the toxicity of different pesticides:

Total usage LI = ∑ , ⋅ all active ingredients TOX AGRAyear

LI is closely related to REXTOX (see 5.1.2) and is driven by the toxicity of individual pesticides. In accordance, LI produce risk trends almost similar to REXTOX. However, LI does not take account of varying buffer zone widths and therefore LI will not be able to predict temporal variations in risks associated with adoption of new buffer zone widths. This was the sole reason why LI was less responsive than REXTOX.

26 6. Conclusion The pesticide risk indicators tested REXTOX, ADSCOR, FA and LI differ markedly in complexity and amount of input data required. However, regardless of their complexity it is mainly use/sales data and toxicity of pesticides that drives the indicators. Thus very simple indicators such as FA and LI produce risk trends very similar to risk trends produced by the more complex and data-intensive indicators REXTOX and ADSCOR.

The indicators FA and LI do not take account of requirements for no-spray bufferzones. Therefore, FA and LI will not be able to predict temporal variations in risks associated with changes in requirement for no-spray buffer zones. This was the main reason why FA and LI were less responsive than e.g. REXTOX and a longer period was needed to obtain significant risk trends. However, by not including buffer zone issues related to farmers’ compliance with requirements for no spray buffer zones calculated risk trends inherently will be more conservative that indicators including buffer zones.

Data on pesticide fate and toxicity are highly variable and the indicators LI, REXTOX and ADSCOR were very sensitive to variation in input data with a maximal variation in values ranging 1-5 orders of magnitude. This did not affect risk trends as long as the same rules were followed each year for selecting values (e.g. so that minimum, maximum or average values is consistently selected). This shows that the indicators are very robust.

Depending on choice of input data the values of a single indicator occasionally showed contradictory year-to-year trends without this affecting risk trend over longer time, and different indicators (calculated on the basis of the same input data) did also show contradictory trends a given year without this affecting risk trend over longer time. These facts emphasise that several years are needed for calculating trends over time.

Data on pesticide sales was used as a substitute for data on actual use of pesticides. This introduced a bias in the indicators in 1995-1996 due to stockpiling caused by tax imposition. However, because several years’ data were available the bias introduced by using sales was of minor importance. Therefore, sales data provide an adequate substitute for actual use when broad national risk trends are requested.

Overall, simple indicators such as FA and LI proved to be adequate for estimating pesticide risk trends in Denmark. To make indicators more responsive to e.g. new requirements for buffer zone as a mean of risk mitigation minor modifications of FA and LI can be included.

Given the complexity of natural systems and the diverse action of pesticides the limitations of simple indicators should be recognised. Indicators are built on information on direct effects, and calculated risk trends will not include information related to interactions in the aquatic environment. Indicators provide estimates of risk trends and not absolute measures of actual risks and indicators ultimately rely on the quality of input data. Hence, insufficient data on use, fate and effects will translate into poor or unreliable estimates of risk trends.

Major gaps were identified with respect to chronic toxicity data for fish, daphnia and algae. As this is likely to bias the risk trends significantly chronic indicators were not included in this test.

27 Annexes

28 Annex A. Sale of pesticides in Denmark Agricultural usage (kg)

1992 1993 1994 1995 1996 1997 1998 1999 2000 Fungicides Azoxystrobin 0 0 0 0 0 0 69651 93458 68775 Benomyl 2598 488 401 0 00000 Carbenazim 19481 6975 3900 10708 00000 Chlorothalonil 17428 6017 5951 5490 6194 19442 21310 9852 5872 Cuprihydroxidchlorid 26136 11951 0 0 00000 Cyprodinil 0 0 0 0 0 0 0 20770 22564 Dimethomorph 000000001878 Fenpropidin 0 0 0 0 0 0 7275 39698 5235 Fenpropimorph 365228 332306 317870 286611 196565 278496 219280 132881 118649 Fluazinam 0 0 0 0 0 0 12540 14147 10482 Iprodion 2114 1489 1530 1611 2441 3413000 Kresoxim-methyl 0 0 0 0 0 0 0 22767 2117 Mancozeb 181565 201360 164018 258269 282411 270307 294482 333211 311455 Maneb 433650 273700 243694 246488 0 97500 64800 0 0 Metalaxyl 3724 4441 4150 4606 00000 Prochloraz 90700 57085 26903 27293 29435 16520 11615 3537 1386 Propamocarb 0 0 0 16095 13536 12286 4404 5476 1300 Propiconazole 109511 88403 84053 97501 66847 85522 40545 18412 21195 Propineb 9464 6877 7819 2237 2854 2658000 Pyrazophos 553 669 846 0 00000 Tebuconazol 0 0 0 0 0 0 22141 14535 40025 Thiabendazol 161 288 212 0 00000 Thiophanat methyl 1794 0 0 0 00000 Triadimenol 22256 12120 7708 6685 00000 Tridemorph 15357 0 0 0 00000 Vinclozolin 1011 0 705 728 1909 3129000 Growth regulators Chlormequat Chloride 250389 317876 237736 289415 71008 84366 151188 207385 193867 Ethephon 18940 7877 4769 16455 10020 15341 17416 12110 8234 Maleinhydrazid 663 1105 1069 1253 1467 378 288 0 536 Mepiquat-chlorid 11175 4365 3666 3337 4270 3788 3398 1366 1495 Trinexapac-ethyl 0 0 0 0 0 0 2270 292 40 Herbicides 2,4-D 40970 29951 33036 15717 56730000 Aclonifen 0 0 0 0 0 0 15648 14304 5748 Asulam 0 1366 2522 2652 1630 1882 1726 2388 2498 Atrazin 16687 42594 665 0 00000 Benazolin 3630 1720 4144 3786 53760000 Bentazon 70496 82329 69352 93326 80577 79317 68918 54081 47773 Bromoxynil 35427 22287 20601 29816 33142 96181 80192 56463 42327 Carbetamid 10706 2890 4605 0 00000 Chloridazon 16640 21057 23195 11765 110370000 Chlorsulforon 923268000000 Clopyralid 16682 14727 15089 22587 11592 10725 12224 9917 7128 Cyaniazin 53208 51129 45968 0 14050000 Desmedipham 4004 3831 4466 3175 1798 1035 912 1196 1686 Dicamba 2127 1058 1144 1372 464 29 2810 591 2553 Dichlorprop-P 240977 171073 139955 129338 49473 3261 2908 1451 1693 Difenzoquat methyl sulfat 25016 18080 11883 11581 29842 17469 16731 13999 0 Diflufenican 0 0 0 0 0 0 0 15735 1456 Diquat 52793 58879 44353 51659 42341 74883 7190 17403 15092 Ethofumesate 57578 50761 35107 52906 31408 22575 21629 15594 15273 Fenoxaprop-P-ethyl 0 0 0 0 0 2444 5728 4522 3872 Flamprop-M-isopropyl 18582 13157 7704 13877 12327 13384 12272 6800 10110 Fluazifop-P-butyl 12089 14139 12636 15321 10845 10704 6222 6684 5030 Fluroxypyr 9130 10368 11206 22378 17790 28270 30852 40191 18389 Glufosinate ammonium 541 709 547 838 661 523 1342 2074 3443 Glyphosate 350944 300017 392549 472763 447788 554373 618496 513398 671731 Glyphosate-Trimesium 0 159390 155368 198526 204650 248278 203283 179203 206442 Haloxyfop ethoxyethylester 2573 4305 4211 7241 3592 6525 5465 3856 3320 Ioxynil 97654 74475 73075 93093 86102 92130 80937 71430 39468 Isoproturon 300876 249235 346767 453168 523547 541365 433725 247525 10275 Isoxaben 1977 930 2602 4107 3926 3942 3427 3563 0 Linuron 11146 3544 4104 11255 10643 9603 8019 6228 7002 MCPA 348206 281516 285793 387396 257543 72903 153215 112380 142113 MCPB 17113 0 0 0 00000

29 Annex A. Agricultural usage (kg), continued

1992 1993 1994 1995 1996 1997 1998 1999 2000 Herbicides (continued) Mechlorprop-P 472092 369034 332545 380565 229469 11703 19237 12217 11594 Metamitron 285390 273210 245112 248896 220097 207298 189382 58436 100065 Methabenzthiazuron 10466 12712 12726 17910 19712 10917 7672 9142 11200 Metribuzin 17814 17133 10452 12670 9317 12389 5334 5691 6676 Metsulfuron methyl 766 692 661 768 223 384 790 1128 753 Napropamide 3845 417 5400 8280 7524 9009 4491 17208 4000 Paraquat 0 993 265 0 00000 Pendimethalin 110078 118712 132926 233726 267328 357928 374158 185438 243256 Phenmedipham 54823 58554 61857 63479 42398 34282 30844 27875 29998 Propachlor 6325 6172 7111 0 256000 Propaquizafop 0 0 0 2394 1253 1436 2049 1932 482 Propyzamid 26644 27537 30580 29709 19883 19170 19050 13182 8867 Prosulfocarb 0 24986 22928 47984 149568 74512 113224 85184 247664 Pyridate 0 3240 19260 0 17991 4841 13383 12717 14768 Terbacil 6700000000 Terbuthylazine 2182 4068 14121 51488 34671 52849 42270 57273 32473 Thifensulfuron-methyl 17 158 123 138 0 632 746 165 200 Tri-allat 2403 136 32 0 0 320 580 700 0 Triasulfuron 0 0 0 283 24 72 255 69 322 Tribenuron methyl 3015 2592 2192 5146 1160 5060 1548 2816 4785 Trifluralin 10248 25957 33936 67475 8654 30505 0 0 581 Triflusulfuron methyl 0 0 0 0 175 768 269 325 389 Insecticides alpha-cypermethrin 3115 3146 1263 4722 1303 609 659 1287 602 Carbofuran 1512 1674 1425 1612 563 1139 286 274 725 Chlorfenvinphos 1572 897 753 629 908 26 89 101 130 Cypermethrin 4524 5152 5040 9360 0 2016 2394 3323 0 Deltamethrin 8087183705442973000 Diazinon 1820 1860 1725 2135 2950000 Dimethoate 62790 51538 37250 64378 19540 32718 36996 21852 24610 Endosulfan 579 456 495 0 00000 Esfenvalerate 5924 5213 4741 7678 1189 2965 751 1985 759 Fenitrothion 12100000000 Lambda-cyhalothrin 767 1184 1194 1548 1020 1000 1171 790 645 Malathion 2688 3219 1850 2945 2466 3576 1434 2275 0 Mercaptodimethur210192400000 Metaldehyd 76 84 39 220 395 530 3684 3697 7987 Mevinphos 188 151 104 198 00000 Oxydemeton-methyl2642272142800000 Phosphamidon64000000000 Phoxim 919816587336188216000 Pirimicarb 39675 30988 37910 66245 6435 5024 5787 5579 1000 Tau-fluvalinate 0 0 0 0 1190 850 2040 4370 4994

30 Annex B. Standard dosages of pesticides. Kg ha-1. 1992 1993 1994 1995 1996 1997 1998 1999 2000 Fungicides Azoxystrobin 0.250 0.250 0.272 Benomyl 0.999 0.244 0.251 Carbenazim 0.770 0.734 0.684 0.878 Chlorothalonil 1.254 1.254 1.240 1.248 1.239 1.246 1.254 1.247 1.249 Cuprihydroxidchlorid 3.788 6.639 Cyprodinil 0.400 0.400 Dimethomorph Fenpropidin 0.750 0.750 0.748 Fenpropimorph 0.865 1.029 1.022 0.383 0.397 0.409 0.483 0.286 0.477 Fluazinam 0.200 0.200 0.200 Iprodion 0.571 0.573 0.588 0.597 0.581 0.588 Kresoxim-methyl Mancozeb 1.889 1.914 1.974 2.022 1.698 1.694 1.635 1.633 1.591 Maneb 1.998 2.001 1.500 1.500 1.500 1.500 Metalaxyl 0.199 0.200 0.200 0.200 Prochloraz 0.573 0.665 0.621 0.571 0.829 0.601 0.624 0.431 0.433 Propamocarb 1.000 1.003 0.999 1.001 0.996 1.000 Propiconazole 0.175 0.161 0.170 0.919 0.446 0.758 1.229 0.829 6.837 Propineb 1.753 1.763 1.738 1.721 1.784 1.772 Pyrazophos 0.614 0.608 0.604 Tebuconazol 0.272 0.263 0.259 Thiabendazol 0.537 0.576 0.530 Thiophanat methyl 0.352 Triadimenol 0.125 0.125 0.125 0.125 Tridemorph 0.844 Vinclozolin 0.532 0.504 0.520 0.502 0.497 Growth regulators Chlormequat Chloride 0.932 0.929 0.931 0.927 0.933 1.139 1.005 0.976 0.980 Ethephon 0.607 0.579 0.691 0.478 0.533 0.478 0.463 0.648 0.664 Maleinhydrazid 2.210 1.842 2.138 2.088 2.096 1.890 2.880 1.787 Mepiquat-chlorid 0.601 0.598 0.601 0.596 0.601 0.601 0.596 0.594 0.598 Trinexapac-ethyl 0.125 0.127 0.133 Herbicides 2.4-D 0.703 0.722 0.923 1.455 2.364 Aclonifen 1.505 1.460 1.474 Asulam 1.518 1.484 1.473 1.482 1.448 1.569 1.493 0.806 Atrazin 0.987 1.954 0.739 Benazolin 0.190 0.307 0.309 0.310 0.311 Bentazon 2.797 0.858 0.817 0.801 0.668 0.499 0.492 0.510 0.523 Bromoxynil 4.542 3.377 1.703 3.923 5.814 1.289 1.001 564.630 0.383 Carbetamid 2.099 2.064 2.093 Chloridazon 2.600 2.600 2.606 2.614 2.628 Chlorsulforon 0.004 0.004 0.004 Clopyralid 0.116 0.129 0.131 0.134 0.156 0.113 0.176 0.215 0.141 Cyaniazin 0.378 0.382 0.342 0.343 Desmedipham Dicamba 0.073 Dichlorprop-P 1.844 1.659 1.449 1.010 1.008 0.836 0.831 0.854 0.847 Difenzoquat methyl sulfat 1.117 1.116 1.111 1.114 1.114 1.113 1.115 1.111 Diflufenican 0.094 0.102 Diquat 0.909 0.904 0.905 0.906 0.907 0.905 0.910 1.360 1.360 Ethofumesate 0.749 0.653 0.919 0.590 0.635 0.640 0.562 0.600 0.491 Fenoxaprop-P-ethyl 0.069 0.069 0.069 0.069 Flamprop-M-isopropyl 0.599 0.598 0.602 0.601 0.601 0.600 0.602 0.602 0.598 Fluazifop-P-butyl 0.316 0.314 0.315 0.315 0.315 0.315 0.314 0.331 0.340 Fluroxypyr 0.188 0.222 0.346 0.321 0.242 0.150 0.183 0.144 0.159 Glufosinate ammonium 0.541 0.591 0.608 0.599 0.601 0.581 0.610 0.593 0.604 Glyphosate 0.873 0.939 1.057 1.058 0.971 1.050 1.101 1.120 1.172 Glyphosate-Trimesium 1.440 1.440 1.440 1.358 1.311 1.256 1.896 1.887 Haloxyfop ethoxyethylester 0.250 0.192 0.192 0.192 0.192 0.192 0.192 0.192 0.169 Ioxynil 0.245 0.239 0.261 0.272 0.233 0.222 0.246 0.252 0.349 Isoproturon 1.224 1.225 1.229 1.227 1.234 1.250 1.250 1.250 Isoxaben 0.080 0.080 0.112 0.095 0.081 0.106 0.200 0.313 Linuron 0.995 0.984 1.001 0.996 1.004 1.000 1.002 1.005 1.000 MCPA 1.481 2.028 1.706 1.577 1.233 1.288 0.892 1.243 1.410 MCPB 1.347

31 Annex B. Standard dosages of pesticides. Kg ha-1 1992 1993 1994 1995 1996 1997 1998 1999 2000 Herbicides (continued) Mechlorprop-P 7.164 7.208 8.549 9.397 23.179 Metamitron 3.502 3.498 3.502 3.501 3.499 3.502 3.501 2.102 2.098 Methabenzthiazuron 2.093 2.119 2.086 2.107 2.097 2.099 2.074 2.126 2.113 Metribuzin 0.500 0.500 0.500 0.501 0.250 0.250 0.250 0.250 0.250 Metsulfuron methyl 0.006 0.006 0.005 0.005 0.005 0.005 0.005 0.005 0.005 Napropamide 0.610 0.596 0.478 0.521 0.485 0.479 0.478 0.479 0.482 Paraquat 0.828 0.883 Pendimethalin 1.101 1.099 1.356 1.357 1.356 1.323 1.323 1.323 1.368 Phenmedipham 0.653 0.711 0.595 0.829 0.833 0.790 0.771 0.845 0.761 Propachlor 4.518 4.748 4.741 Propaquizafop 0.122 0.123 0.123 0.123 0.125 0.118 Propyzamid 0.645 0.645 0.645 0.646 0.646 0.645 0.646 0.499 0.501 Prosulfocarb 2.807 2.796 2.806 2.854 2.801 2.803 2.802 2.798 Pyridate 1.906 0.900 0.900 0.896 0.898 0.480 0.479 Terbacil 0.335 Terbuthylazine 1.148 1.162 0.759 1.195 1.313 1.106 0.899 1.176 27.061 Thifensulfuron-methyl 0.007 0.007 0.008 0.008 0.007 0.008 0.008 0.008 Tri-allat 24.030 1.360 1.600 1.600 1.450 1.750 Triasulfuron 0.004 0.004 0.004 0.004 0.003 0.004 Tribenuron methyl 0.006 0.006 0.007 0.008 0.007 0.008 0.010 0.008 0.008 Trifluralin 1.708 0.868 0.853 0.872 0.874 0.820 0.830 Triflusulfuron methyl 0.045 0.045 0.041 0.045 0.045 Insecticides alpha-cypermethrin 0.013 0.013 0.013 0.012 0.013 0.013 0.015 0.012 0.012 Carbofuran 0.687 0.644 0.679 0.672 0.704 0.670 0.715 0.685 0.659 Chlorfenvinphos 0.983 0.997 2.510 3.145 1.009 0.890 1.010 1.300 Cypermethrin 0.040 0.040 0.040 0.040 0.040 0.040 0.040 Deltamethrin 0.007 0.007 0.007 0.006 0.006 0.007 Diazinon 3.640 3.720 4.313 4.270 2.950 Dimethoate 0.301 0.299 0.307 0.298 0.304 0.292 0.295 0.306 0.304 Endosulfan 0.526 0.570 0.550 Esfenvalerate 0.013 0.014 0.010 0.010 0.010 0.010 0.010 0.010 0.010 Fenitrothion 0.605 Lambda-cyhalothrin 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 Malathion 0.584 0.555 0.561 0.566 0.560 0.559 0.896 0.910 Mercaptodimethur 0.105 0.000 0.095 0.120 Metaldehyd 0.760 0.840 0.390 0.550 0.494 0.482 0.505 0.500 0.499 Mevinphos 0.145 0.151 0.149 0.152 Oxydemeton-methyl 0.126 0.119 0.126 0.140 Phosphamidon 0.256 Phoxim 4.595 4.080 5.870 3.360 Pirimicarb 0.136 0.135 0.136 0.136 0.136 0.136 0.134 0.134 0.135 Tau-fluvalinate 0.060 0.060 0.060 0.060 0.060

32 Annex C. Calculated Sprayed area (1000 ha) using standard dosage levels 1992 1993 1994 1995 1996 1997 1998 1999 2000 Fungicides Azoxystrobin 278.6 373.8 252.9 Benomyl 2.6 2.0 1.6 Carbenazim 25.3 9.5 5.7 12.2 Chlorothalonil 13.9 4.8 4.8 4.4 5.0 15.6 17.0 7.9 4.7 Cuprihydroxidchlorid 6.9 1.8 Cyprodinil 51.9 56.4 Dimethomorph Fenpropidin 9.7 52.9 7.0 Fenpropimorph 422.3 322.9 311.0 748.4 495.4 681.0 454.0 463.9 248.7 Fluazinam 62.7 70.7 52.4 Iprodion 3.7 2.6 2.6 2.7 4.2 5.8 Kresoxim-methyl Mancozeb 96.1 105.2 83.1 127.7 166.3 159.6 180.1 204.1 195.8 Maneb 217.0 136.8 162.5 164.3 65.0 43.2 Metalaxyl 18.7 22.2 20.8 23.0 Prochloraz 158.2 85.8 43.3 47.8 35.5 27.5 18.6 8.2 3.2 Propamocarb 16.1 13.5 12.3 4.4 5.5 1.3 Propiconazole 626.2 547.6 494.8 106.1 149.9 112.8 33.0 22.2 3.1 Propineb 5.4 3.9 4.5 1.3 1.6 1.5 Pyrazophos 0.9 1.1 1.4 Tebuconazol 81.3 55.2 154.8 Thiabendazol 0.3 0.5 0.4 Thiophanat methyl 5.1 Triadimenol 178.0 96.9 61.7 53.5 Tridemorph 18.2 Vinclozolin 1.9 1.4 1.4 3.8 6.3 Growth regulators Chlormequat Chloride 268.7 342.3 255.4 312.1 76.1 74.1 150.4 212.5 197.9 Ethephon 31.2 13.6 6.9 34.4 18.8 32.1 37.6 18.7 12.4 Maleinhydrazid 0.3 0.6 0.5 0.6 0.7 0.2 0.1 0.3 Mepiquat-chlorid 18.6 7.3 6.1 5.6 7.1 6.3 5.7 2.3 2.5 Trinexapac-ethyl 18.2 2.3 0.3 Herbicides 2.4-D 58.3 41.5 35.8 10.8 2.4 Aclonifen 10.4 9.8 3.9 Asulam 0.9 1.7 1.8 1.1 1.3 1.1 1.6 3.1 Atrazin 16.9 21.8 0.9 Benazolin 19.1 5.6 13.4 12.2 17.3 Bentazon 25.2 95.9 84.9 116.5 120.7 158.8 140.1 106.0 91.3 Bromoxynil 7.8 6.6 12.1 7.6 5.7 74.6 80.1 0.1 110.4 Carbetamid 5.1 1.4 2.2 Chloridazon 6.4 8.1 8.9 4.5 4.2 Chlorsulforon 23.0 8.0 17.0 Clopyralid 143.7 114.5 114.9 169.0 74.4 95.1 69.4 46.1 50.5 Cyaniazin 140.8 134.0 134.4 4.1 Desmedipham Dicamba 0.4 Dichlorprop-P 130.7 103.1 96.6 128.1 49.1 3.9 3.5 1.7 2.0 Difenzoquat methyl sulfat 22.4 16.2 10.7 10.4 26.8 15.7 15.0 12.6 Diflufenican 167.8 14.3 Diquat 58.1 65.1 49.0 57.0 46.7 82.7 7.9 12.8 11.1 Ethofumesate 76.9 77.7 38.2 89.6 49.5 35.3 38.5 26.0 31.1 Fenoxaprop-P-ethyl 35.4 83.0 65.5 56.1 Flamprop-M-isopropyl 31.0 22.0 12.8 23.1 20.5 22.3 20.4 11.3 16.9 Fluazifop-P-butyl 38.2 45.0 40.1 48.6 34.4 34.0 19.8 20.2 14.8 Fluroxypyr 48.5 46.8 32.4 69.7 73.6 187.9 168.9 278.7 115.6 Glufosinate ammonium 1.0 1.2 0.9 1.4 1.1 0.9 2.2 3.5 5.7 Glyphosate 402.0 319.5 371.3 447.0 461.2 528.2 561.7 458.5 573.1 Glyphosate-Trimesium 110.7 107.9 137.9 150.7 189.4 161.9 94.5 109.4 Haloxyfop ethoxyethylester 10.3 22.4 21.9 37.7 18.7 33.9 28.4 20.1 19.6 Ioxynil 399.1 311.3 280.1 342.4 369.4 414.6 329.6 282.9 113.2 Isoproturon 245.9 203.4 282.1 369.2 424.3 433.1 347.0 198.0 Isoxaben 24.7 11.6 23.2 43.3 48.3 37.1 17.1 11.4 Linuron 11.2 3.6 4.1 11.3 10.6 9.6 8.0 6.2 7.0 MCPA 235.1 138.8 167.5 245.7 208.9 56.6 171.8 90.4 100.8 MCPB 12.7

33 Annex C. Calculated Sprayed area (1000 ha), continued 1992 1993 1994 1995 1996 1997 1998 1999 2000 Herbicides (continued) Mechlorprop-P 65.9 51.2 38.9 40.5 9.9 Metamitron 81.5 78.1 70.0 71.1 62.9 59.2 54.1 27.8 47.7 Methabenzthiazuron 5.0 6.0 6.1 8.5 9.4 5.2 3.7 4.3 5.3 Metribuzin 35.6 34.3 20.9 25.3 37.3 49.6 21.3 22.8 26.7 Metsulfuron methyl 130.9 115.3 132.0 153.6 44.6 76.8 158.0 225.6 150.6 Napropamide 6.3 0.7 11.3 15.9 15.5 18.8 9.4 35.9 8.3 Paraquat 1.2 0.3 Pendimethalin 100.0 108.0 98.0 172.3 197.1 270.6 282.9 140.2 177.8 Phenmedipham 84.0 82.4 104.0 76.6 50.9 43.4 40.0 33.0 39.4 Propachlor 1.4 1.3 1.5 Propaquizafop 19.6 10.2 11.7 16.7 15.4 4.1 Propyzamid 41.3 42.7 47.4 46.0 30.8 29.7 29.5 26.4 17.7 Prosulfocarb 8.9 8.2 17.1 52.4 26.6 40.4 30.4 88.5 Pyridate 1.7 21.4 20.0 5.4 14.9 26.5 30.8 Terbacil 0.2 Terbuthylazine 1.9 3.5 18.6 43.1 26.4 47.8 47.0 48.7 1.2 Thifensulfuron-methyl 2.3 21.1 16.4 18.4 84.3 91.2 20.2 24.4 Tri-allat 0.1 0.1 0.0 0.2 0.4 0.4 Triasulfuron 70.8 6.0 18.0 63.8 19.8 91.2 Tribenuron methyl 482.4 402.1 320.1 686.1 154.7 633.7 156.5 364.4 624.7 Trifluralin 6.0 29.9 39.8 77.4 9.9 37.2 0.7 Triflusulfuron methyl 3.9 17.1 6.6 7.2 8.6 Insecticides alpha-cypermethrin 249.2 251.6 101.0 377.8 104.2 48.7 43.7 103.0 48.2 Carbofuran 2.2 2.6 2.1 2.4 0.8 1.7 0.4 0.4 1.1 Chlorfenvinphos 1.6 0.9 0.3 0.2 0.9 0.1 0.1 0.1 Cypermethrin 112.9 129.1 126.0 234.0 50.4 59.9 83.1 Deltamethrin 124.1 110.4 56.9 83.7 4.5 11.2 Diazinon 0.5 0.5 0.4 0.5 0.1 Dimethoate 208.9 172.6 121.3 215.8 64.2 111.9 125.2 71.5 81.0 Endosulfan 1.1 0.8 0.9 Esfenvalerate 455.0 373.1 463.6 767.8 118.9 296.5 75.1 198.5 75.9 Fenitrothion 0.2 Lambda-cyhalothrin 96.1 147.3 149.3 193.5 127.5 125.0 146.4 98.8 80.6 Malathion 4.6 5.8 3.3 5.2 4.4 6.4 1.6 2.5 Mercaptodimethur 0.2 0.1 0.2 0.2 Metaldehyd 0.1 0.1 0.1 0.4 0.8 1.1 7.3 7.4 16.0 Mevinphos 1.3 1.0 0.7 1.3 Oxydemeton-methyl 2.1 1.9 1.7 0.2 Phosphamidon 2.5 Phoxim 0.2 0.2 0.1 0.1 Pirimicarb 292.6 228.7 279.5 488.4 47.4 37.0 43.2 41.7 7.4 Tau-fluvalinate 19.8 14.2 34.0 72.8 83.2

34 Annex D No-spray zones (m) required for risk mitigation 1992 1993 1994 1995 1996 1997 1998 1999 2000 Fungicides Azoxystrobin 000000000 Benomyl 000000000 Carbenazim 0 0 0 0 00000 Chlorothalonil 0 0 0 0 010101010 Cuprihydroxidchlorid000000000 Cyprodinil 0 0 0 0 0 0 0 10 10 Dimethomorph 0000000020 Fenpropidin 0 0 0 0 0 0 0 20 20 Fenpropimorph 0 0 0 0 0 20 20 20 20 Fluazinam 0 0 0 0 0 0 0 20 20 Iprodion 000000000 Kresoxim-methyl 0000000020 Mancozeb 000000000 Maneb 0 0 0 10 10 10 10 10 10 Metalaxyl 000000000 Prochloraz 0 0 10 10 10 10 10 10 10 Propamocarb 0 0 0101010101010 Propiconazole 0 0 0 0 0 20 20 20 20 Propineb 000000000 Pyrazophos 0 0 0 0 00000 Tebuconazol 0 0 0 0 0 0 10 10 10 Thiabendazol 0 0 0 0 00000 Thiophanat methyl 0 0 0 0 00000 Triadimenol 0 0 0 0 00000 Tridemorph 000000000 Vinclozolin 000000000 Growth regulators Chlormequat Chloride 0 0 0 0 10 10 10 10 10 Ethephon 0 0 0 0 00000 Maleinhydrazid 000000000 Mepiquat-chlorid 0 0 0 0 10 10 10 10 10 Trinexapac-ethyl 0 0 0 0 00200 Herbicides 2.4-D 000000000 Aclonifen 0 0 0 0 0 0 0 20 20 Asulam 000000000 Atrazin 101010101010101010 Benazolin 0 0 0 0 00000 Bentazon 0 0 0 0 10 10 10 10 10 Bromoxynil 0 0 0 0 10 20 20 20 20 Carbetamid 000000000 Chloridazon 01010101010101010 Chlorsulforon 000000000 Clopyralid 000000000 Cyaniazin 000000000 Desmedipham 101010101010101010 Dicamba 000000000 Dichlorprop-P 000000000 Difenzoquat methyl sulfat 0 10 10 10 10 10 10 10 10 Diflufenican 0 0 0 0 0 0 0 10 0 Diquat 0 0 0 0 00000 Ethofumesate 101010101010101010 Fenoxaprop-P-ethyl 0 0 0 0 0 10 10 10 10 Flamprop-M-isopropyl 0 10 10 10 10 10 10 10 10 Fluazifop-P-butyl 0 0 0 0 0 0 0 10 10 Fluroxypyr 000000000 Glufosinate ammonium000000000 Glyphosate 0 0 0 0 00000 Glyphosate-Trimesium 0 0 0 0 00000 Haloxyfop ethoxyethylester 0 0 0 10 10 10 10 10 10 Ioxynil 0 0 0 0 10 10 10 10 10 Isoproturon 000000000 Isoxaben 0 0 0 0 00000 Linuron 000000000 MCPA 000000000 MCPB 000000000

35 Annex D. No-spray zones required for risk mitigation (m), continued 1992 1993 1994 1995 1996 1997 1998 1999 2000 Herbicides (continued) Mechlorprop-P 000000000 Metamitron 000000000 Methabenzthiazuron 0 0 0 0 00000 Metribuzin 0 0 0 0 0 0 10 10 10 Metsulfuron methyl 0 0 0 0 020202020 Napropamide 0 0 0 0 00000 Paraquat 0 0 0 0 00000 Pendimethalin 10 10 10 10 10 10 10 10 10 Phenmedipham 101010101010101010 Propachlor 0 0 0 0 00000 Propaquizafop 0 0 0 0 0 0 10 10 10 Propyzamid 000000000 Prosulfocarb 0 0 10 10 10 10 10 10 10 Pyridate 000000000 Terbacil 000000000 Terbuthylazine 10 10 10 10 10 10 10 10 10 Thifensulfuron-methyl000000000 Tri-allat 000000000 Triasulfuron 0 0 0101010101010 Tribenuron methyl 0 0 0 0 00000 Trifluralin 01010101010101010 Triflusulfuron methyl0 0 0 0 010101010 Insecticides alpha-cypermethrin 0 0 0 0 0 0 0 20 20 Carbofuran 01010101010101010 Chlorfenvinphos 0 0 0 0 00000 Cypermethrin 0 0 0 0 020202020 Deltamethrin 000000000 Diazinon 000000000 Dimethoate 0 0 10 10 10 10 10 10 10 Endosulfan 0 0 0 0 00000 Esfenvalerate 0 0 0 0 0 0 0 20 20 Fenitrothion 000000000 Lambda-cyhalothrin 0 0 0 10 10 10 10 10 10 Malathion 0 010101010101010 Mercaptodimethur000000000 Metaldehyd 0 0 0 0 00000 Mevinphos 0 0 0 0 00000 Oxydemeton-methyl01010101010101010 Phosphamidon 000000000 Phoxim 000000000 Pirimicarb 01010101010101010 Tau-fluvalinate 0 0 0 0 0 20 20 20 20

36 Annex E. Data availability and gaps

Pesticide Fate Acute Toxicity Chronic Toxicity Fish96hr Daphnia48hr Algae96hr Fish21day Daphnia21d Algae96hr DT50 Kd LC50 EC50 EC50 NOEC NOEC50 NOEC Fungicider Azoxystrobin + + + + + + + + Benomyl + + + + + - - - Carbenazim + - + + + - - - Chlorothalonil + + + + + - + - Cuprihydroxidchlorid ------Cyprodinil + + + + + + - - Dimethomorph ------Fenpropidin + + + + + + - + Fenpropimorph + + + + + + + + Fluazinam + + + + + - + - Iprodion + + + + + - - - Kresoxim-methyl + + + + + + + - Mancozeb + + + + + - + - Maneb + + + + + - - - Metalaxyl - - - + + - - - Prochloraz + + + + + - + + Propamocarb + + + + + - + - Propiconazole + + + + + - - - Propineb + - + + + - - - Pyrazophos + + + + + - - - Tebuconazol + + + + + - - - Thiabendazol + + + + + - - - Thiophanat methyl + - + + - - - - Triadimenol + + + + + - - - Tridemorph + + + + + - - - Vinclozolin + + - + + - - - Growth regulator Chlormequat Chloride + + + + + + + + Ethephon + + + + + - + + Maleinhydrazid + + + - + - - - Mepiquat-chlorid + + + + + - - - Trinexapac-ethyl + + + + + - - + Herbicides 2,4-D + + + + + - - - Aclonifen + + + + + + - + Asulam++++ + --- Atrazin++++ + --- Benazolin + + + + + - - - Bentazon + + + + + - - + Bromoxynil + + + + + - - + Carbetamid + + + + + - - - Chloridazon + + + + + - - - Chlorsulforon + + - + - - - - Clopyralid + + + + + - - + Cyaniazin + + + + + - - - Desmedipham + + + + + + + - Dicamba + + + + + + - - Dichlorprop-P + + + + + + + + Difenzoquat methyl sulfat + + + + + - - - Diflufenican + + + - + + + - Diquat + + + + + + + - Ethofumesate + + + + + - + - Fenoxaprop-P-ethyl + + + + + - + + Flamprop-M-isopropyl + + + + + - + - Fluazifop-P-butyl + + + + - + + + Fluroxypyr + + + + + + + + Glufosinate ammonium + - + + + - - - Glyphosate + + + + + + + + Glyphosate-Trimesium + + + + + + - + Haloxyfop ++++ + --- ethoxyethylester Ioxynil++++ + --- Isoproturon + + + + + - - -

37 Annex E. Data availability and gaps (continued)

Pesticide Fate Acute Toxicity Chronic Toxicity Fish96hr Daphnia48hr Algae96hr Fish21day Daphnia21d Algae96hr DT50 Kd LC50 EC50 EC50 NOEC NOEC50 NOEC Herbicides (continued) Isoxaben + + - + + - + - Linuron + + + + - - - - MCPA + + + + + + + - MCPB--+- - --- Mechlorprop-P + + + + + + + - Metamitron + + + + + - + - Methabenzthiazuron + + + + + - + - Metribuzin + + + + + + + - Metsulfuron methyl + + + + + - - + Napropamide + + + + + - - - Paraquat + + + - - - - - Pendimethalin + + + + + - - - Phenmedipham + + + + + - + + Propachlor + + + + - - - - Propaquizafop + + + - - + + + Propyzamid + + + - + - - - Prosulfocarb + + + + + - + - Pyridate + + + + + - - - Terbacil + + + + + - - - Terbuthylazine + + + + + + + + Thifensulfuron-methyl + + - + + - + - Tri-allat + + + + + - - - Triasulfuron + + - - + - - - Tribenuron methyl + + - + + - - - Trifluralin + + + + + - - - Triflusulfuron methyl + + + + + + + + Insecticides alpha-cypermethrin + + + + - - + - Carbofuran + + + + + - + - Chlorfenvinphos + - + + + - - - Cypermethrin + + + + - - - + Deltamethrin + + + + - - - - Diazinon + + + + + - - - Dimethoate + + + + + + + + Endosulfan + + + + + - - - Esfenvalerate + + + + + - + - Fenitrothion + + + + + - - - Lambda-cyhalothrin + + + - + - - + Malathion + + + + + - - - Mercaptodimethur + + + - + - - - Metaldehyd + + + - + - - - Mevinphos + - + + - - - - Oxydemeton-methyl + + + + + - - - Phosphamidon - - + + + - - - Phoxim - - + + + - - - Pirimicarb + + + + + - + + Tau-fluvalinate + + + - - - + -

38 Annex F. Fate data DT50 (days)

Pesticide Funigicides Azoxystrobin 279.00 54.00 164.00 85.00 94.00 57.00 60.00 Benomyl 0.79 Carbenazim 30.87 30.18 42.64 46.16 25.15 52.87 41.99 41.09 28.17 35.29 Chlorothalonil 7.97 9.12 10.34 5.77 6.66 0.06 0.04 Cyprodinil 21.40 23.70 122.00 44.00 40.00 33.00 24.20 50.70 79.80 13.00 41.70 19.00 22.10 106.00 165.00 125.50 177.70 Fenpropidin 96.00 97.00 50.00 94.00 93.00 59.00 141.00 187.00 233.00 65.00 21.00 Fenpropimorph 36.00 13.25 5.06 20.00 Fluazinam 55.00 Iprodion 52.07 14.66 37.81 53.54 43.14 48.16 32.65 46.57 39.48 21.79 Kresoxim-methyl 44.00 511.00 25.00 22.00 294.00 24.00 129.00 22.00 23.00 1.30 0.90 1.20 0.80 Mancozeb 6.35 6.32 8.12 11.05 6.53 6.74 6.55 7.36 11.06 6.81 Maneb 6.46 13.71 10.83 13.93 4.90 12.41 13.21 18.01 17.88 10.04 Prochloraz 228.00 92.00 171.00 155.00 115.00 21.00 Propamocarb 10.00 18.00 58.00 12.00 23.00 44.00 14.00 22.00 24.00 Propiconazole 43.00 47.00 70.00 309.00 255.00 41.00 56.00 128.00 316.00 42.00 430.00 72.00 70.00 426.80 Propineb 11.07 4.74 9.00 0.63 1.35 3.97 4.76 2.46 2.26 2.47 Pyrazophos 24.17 15.07 33.14 40.18 12.13 29.79 43.86 31.35 33.07 35.86 Tebuconazol 489.00 129.00 17.57 21.00 Thiabendazol 672.95 623.18 576.53 567.97 595.84 711.61 553.52 693.72 629.69 582.32 Thiophanat methyl 2.56 3.46 3.88 4.17 3.35 3.80 3.97 3.46 2.49 4.28 Triadimenol 192.57 345.92 199.07 332.37 243.35 257.36 206.12 282.71 253.30 277.69 Tridemorph 48.78 28.79 38.49 40.16 25.84 20.72 41.56 39.89 35.11 39.78 Vinclozolin 10.89 14.11 10.68 33.10 6.73 2.71 22.35 25.19 0.00 25.42 Growth regulator Chlormequat Chloride 10.50 8.00 2.38 3.28 2.64 Ethephon 4.00 8.00 10.00 25.00 22.00 7.10 7.00 7.50 Maleinhydrazid 0.46 Mepiquat-chlorid 11.00 10.00 2.72 21.06 Trinexapac-ethyl 0.22 0.22 0.09 0.17 3.30 2.10 3.40 5.20 3.90 5.50 Herbicides 2.4-D 210.62 166.47 143.99 116.50 67.21 72.63 46.05 252.85 10.77 110.91 Aclonifen 91.80 40.00 217.00 217.00 84.00 49.00 70.00 76.00 82.00 15.40 16.10 Asulam 28 Atrazin 39 Benazolin 3 Bentazon 17.00 17.00 38.00 38.00 73.00 24.00 31.00 65.00 Bromoxynil 0.75 0.75 0.63 0.63 2.40 2.10 2.40 1.80 0.78 0.30 0.84 0.44 1.42 0.63 1.19 0.78 Carbetamid 12.10 17.72 9.85 12.83 6.70 6.53 9.78 14.40 14.30 17.76 Chloridazon 27.33 4.27 11.53 22.66 4.01 24.33 18.53 34.14 34.27 12.33 Chlorsulforon 39.31 38.79 30.93 40.72 34.77 34.81 37.07 34.47 36.11 33.23 Clopyralid 62.00 12.00 178.00 226.00 23.00 48.00 11.00 30.00 350.00 285.00 102.00 68.00 44.00 28.00 24.00 17.00 157.00 82.00 Cyaniazin 52.29 56.55 59.77 44.30 61.04 44.82 77.31 49.45 69.34 25.93 Desmedipham 5.30 12.40 5.50 20.50 8.00 3.40 0.80 Dicamba 2,90 6,00 39,80 45,50 20,20 Annex F. DT50 Continued Pesticide Herbicides (continued) Dichlorprop-P 21.00 25.00 20.45 9.00 14.00 21.00 20.00 Difenzoquat methyl sulfat 30.00 Diflufenican 168.00 294.00 875.00 728.00 72.00 82.00 Diquat 1308.7 1098.3 1282.0 1159.5 1181.7 1257.0 1295.7 1246.0 1226.1 1286.0 Ethofumesate 50.00 125.00 147.00 140.00 105.00 Fenoxaprop-P-ethyl 3.00 6.00 0.60 0.60 0.70 0.70 Flamprop-M-isopropyl 68.80 87.20 233.60 56.00 217.00 112.00 Fluazifop-P-butyl 3 Fluroxypyr 2.38 2.28 3.19 5.05 7.20 4.29 30.00 832.00 45.00 55.00 12.00 12.00 23.00 7.00 15.00 12.00 7.00 70.00 Glufosinate ammonium 42.00 32.00 15.00 23.00 21.00 22.00 15.00 Glyphosate 25.40 15.40 11.20 14.00 25.90 25.20 25.90 7.50 8.50 3.60 1.40 27.40 3.70 146.30 14.40 Glyphosate-Trimesium 3.00 3.00 12.00 9.00 67.00 20.00 5.00 6.00 70.00 6.00 1.00 24.00 46.00 58.00 62.00 3.00 8.00 11.00 15.00 35.00 Haloxyfop 47.00 51.00 100.00 27.00 56.00 47.00 1.00 1.00 1.00 98.00 105.00 113.00 230.00 ethoxyethylester Ioxynil 1.50 2.00 3.50 6.50 7.50 75.00 2.50 15.50 Isoproturon 52.93 64.50 6.76 26.02 23.25 48.51 37.39 42.32 39.67 29.80 Isoxaben 131.15 179.95 323.30 521.00 29.60 989.90 16.80 4466.0 11.30 Linuron 291.90 87.50 275.80 64.40 187.60 56.00 91.00 136.50 73.90 74.10 35.20 86.00 77.80 68.00 76.00 82.40 50.80 69.80 81.20 57.40 66.70 49.30 24.90 22.30 56.20 23.70 10.00 19.00 16.00 28.00 MCPA 32.03 11.52 35.60 18.41 44.48 43.70 0.00 45.51 24.97 29.65 Mechlorprop-P 13.30 11.73 12.23 5.91 18.50 11.50 Metamitron 3.00 3.20 3.50 Methabenzthiazuron 26.25 65.71 31.00 Metribuzin 106.00 70.00 50.00 50.00 21.00 42.00 20.00 56.00 58.00 Metsulfuron methyl 11.00 10.00 Napropamide 56.00 84.00 670.00 230.00 Paraquat 2750.7 6542.2 3701.7 5943.2 5171.8 4890.7 4374.4 7006.9 6098.6 5808.4 Pendimethalin 72.00 87.00 132.00 127.00 172.00 Phenmedipham 43.78 21.46 41.12 32.61 24.92 32.27 23.64 35.85 31.76 39.13 Propachlor 121.31 132.16 115.71 125.85 123.21 147.66 151.71 145.01 164.87 133.32 Propaquizafop 28.00 47.00 22.00 42.00 32.00 39.00 10.00 4.00 17.00 139.00 110.00 20.00 20.00 Propyzamid 23.83 25.50 28.45 32.57 24.39 31.14 19.66 18.70 23.88 13.84 Prosulfocarb 13.00 13.00 Pyridate 87.00 0.82 14.00 23.00 23.00 26.00 60.00 10.00 20.00 22.00 27.00 Terbacil 266.16 181.01 375.67 222.47 298.57 206.01 292.13 225.68 232.99 195.97 Terbuthylazine 77.00 63.00 121.00 108.00 94.00 66.00 169.00 79.00 252.00 272.00 456.00 133.00 138.00 73.00 149.80 74.20 6.10 79.80 6.50 33.10 32.30 246.40 19.30 137.10 61.00 55.00 78.70 137.80 60.90 6.00 6.00 73.00 33.00 18.00 9.00 167.00 88.00 Thifensulfuron-methyl 2.00 6.00 1.67 3.39 Tri-allat 12.00 4.00 12.00 5.00 10.00 4.00 12.00 3.00 20.00 Triasulfuron 325.50 66.50 105.00 121.50 52.80 44.90 38.60 70.90 73.50 193.90 205.10 189.00 247.10 38.00 Tribenuron methyl 19.20 20.90 29.70 30.10 3.00 12.00 Trifluralin 122.61 83.06 159.78 14.04 145.84 169.58 305.91 164.98 205.61 0.00 Triflusulfuron methyl 6.00 6.30 5.50 14.40 6.10 8.40 7.70 17.20 30.00 39.00 18.00 21.00

40 Annex F. DT50 Continued

Pesticide Insecticides alpha-cypermethrin 189.00 91.00 Carbofuran 25.00 160.00 3.50 4.50 Chlorfenvinphos 58.00 55.00 28.00 Cypermethrin 7.00 14.00 21.00 14.00 7.00 21.00 49.00 56.00 Deltamethrin 37.27 19.30 19.70 33.74 40.63 46.94 28.90 45.30 24.59 31.44 Diazinon 31.11 36.10 47.00 38.83 32.47 27.58 50.70 38.36 32.46 31.38 Dimethoate 2.40 2.40 2.00 4.10 Endosulfan 45.82 39.88 56.17 67.82 67.20 65.47 50.04 48.61 37.69 56.56 Esfenvalerate 15.00 91.50 46.00 39.20 178.80 61.20 26.90 67.80 Fenitrothion 18.04 24.62 25.22 17.37 24.12 21.15 19.49 17.81 16.61 22.45 Lambda-cyhalothrin 22.00 82.00 42.00 56.00 74.00 Malathion 1.00 0.21 Mercaptodimethur 40.02 32.19 32.75 37.96 34.07 40.92 35.61 34.39 35.40 30.59 Metaldehyd 0.00 46.47 124.09 36.15 12.06 39.64 79.39 108.67 90.45 92.54 Mevinphos 0.92 1.37 1.23 1.48 1.48 0.55 0.82 0.88 0.42 1.19 Oxydemeton-methyl 3.57 1.31 2.22 3.09 3.92 2.36 3.34 1.76 2.36 2.65 Pirimicarb 121.06 3.46 67.97 21.11 75.09 0.00 73.29 157.96 74.24 0.00 Tau-fluvalinate 6.00 6.00 8.00 8.30 14.90

41 Annex G. Fate data Kd Pesticide Funigicides Azoxystrobin 7.90 9.50 1.50 4.00 6.20 15.00 Benomyl 24.72 25.56 12.97 23.65 16.51 18.45 19.05 29.85 27.55 21.27 Chlorothalonil 0.00 23.46 0.00 0.00 22.17 32.60 113.29 44.23 57.70 74.56 Cyprodinil 12.30 18.89 25.41 74.91 355.55 16.30 14.20 31.20 24.30 Fenpropidin 117.10 64.20 43.50 40.30 24.10 17.40 Fenpropimorph 34.47 73.73 22.62 Fluazinam 11.12 43.48 27.19 37.88 Iprodion 21.68 8.61 11.21 9.56 13.41 16.13 11.92 20.45 8.66 13.61 Kresoxim-methyl 26.01 77.38 36.16 59.19 Mancozeb 11.67 9.89 7.26 10.13 Maneb 0.47 0.09 0.08 0.14 Prochloraz 86.80 101.70 Propamocarb 5.83 1.26 0.67 0.85 5.20 Propiconazole 13.00 12.75 11.97 12.24 12.40 16.15 8.70 12.46 11.44 9.77 Propyzamid 8.05 10.10 3.47 4.85 3.15 5.16 Pyrazophos 63.56 66.00 68.99 69.30 61.90 69.36 72.76 64.75 67.23 65.24 Tebuconazol 18.77 22.27 11.83 23.76 12.70 10.84 Thiabendazol 43.11 53.95 58.66 31.65 32.86 50.61 52.87 23.31 39.00 29.88 Thifensulfuron-methyl 0.08 0.19 1.38 1.25 Triadimenol 3.6 Tridemorph 87.34 134.97 69.19 145.89 149.56 126.24 101.87 60.43 68.49 52.35 Vinclozolin 2.52 4.94 2.48 4.73 5.11 2.95 2.58 1.71 5.12 1.59 Growth regulator Chlormequat Chloride 24.35 Ethephon 57.33 53.13 29.77 2.37 7.17 5.93 6.55 17.10 8.85 Maleinhydrazid 1.15 1.04 1.05 0.66 1.05 0.72 0.99 0.90 0.81 0.72 Mepiquat-chlorid 133.65 17.06 5.74 74.07 3.90 51.70 Trinexapac-ethyl 17.77 1.50 0.66 0.67 Herbicides 2.4-D 210.62 166.47 143.99 116.50 67.21 72.63 46.05 252.85 10.77 110.91 Aclonifen 91.80 40.00 217.00 217.00 84.00 49.00 70.00 76.00 82.00 15.40 16.10 Asulam 28 Atrazin 39 Benazolin 3 Bentazon 17.00 17.00 38.00 38.00 73.00 24.00 31.00 65.00 Bromoxynil 0.75 0.75 0.63 0.63 2.40 2.10 2.40 1.80 0.78 0.30 0.84 0.44 1.42 0.63 1.19 0.78 Carbetamid 12.10 17.72 9.85 12.83 6.70 6.53 9.78 14.40 14.30 17.76 Chloridazon 27.33 4.27 11.53 22.66 4.01 24.33 18.53 34.14 34.27 12.33 Chlorsulforon 39.31 38.79 30.93 40.72 34.77 34.81 37.07 34.47 36.11 33.23 Clopyralid 62.00 12.00 178.00 226.00 23.00 48.00 11.00 30.00 350.00 285.00 102.00 68.00 44.00 28.00 24.00 17.00 157.00 82.00 Cyaniazin 52.29 56.55 59.77 44.30 61.04 44.82 77.31 49.45 69.34 25.93 Desmedipham 5.30 12.40 5.50 20.50 8.00 3.40 0.80

42 Annex F. Kd Continued Pesticide Herbicides (continued) Dicamba 0.16 0.10 0.53 0.07 0.21 Dichlorprop-P 0.48 0.22 0.10 0.24 Difenzoquat methyl sulfat 181.00 636.00 1093 2680 Diflufenican 33.90 13.50 39.80 48.90 Diquat 5000 1200 17000 85.00 16000 3500 5000 4500 42000 22000 2000 7000 27000 38000 37000 31000 43000 14000 12000 6000 25000 16000 92000 53000 14000 28000 47000 43000 57000 49000 25000 25000 Ethofumesate 1.13 0.73 2346 5315 6155 3.14 1.11 21.70 Fenoxaprop-P-ethyl 104.00 57.00 82.00 149.00 Flamprop-M-isopropyl 0.94 5.05 1.74 Fluazifop-P-butyl 0.26 0.17 0.23 0.14 Fluroxypyr 0.29 0.17 0.00 0.00 0.00 0.00 Glyphosate 90.00 70.00 62.00 22.00 175.00 115.00 80.00 68.00 30.00 205.00 Glyphosate-Trimesium 20.51 11.98 8.09 22.06 376.00 55.70 31.50 2060 Haloxyfop 0.79 1.80 3.30 0.10 0.73 0.25 0.60 0.41 0.73 3.76 1.81 ethoxyethylester Ioxynil 3.50 5.10 6.00 182.00 Isoproturon 0.72 0.80 0.84 0.85 0.81 0.75 0.88 0.87 0.83 0.82 Isoxaben 5.70 0.81 2.48 4.41 6.63 2.18 Linuron 8.25 MCPA 0.75 Mechlorprop-P 4.50 3.50 3.30 0.30 0.69 0.43 0.20 Metamitron 1.43 2.36 1.20 1.07 1.66 0.68 1.82 1.04 1.54 0.36 1.49 5.88 1.23 0.70 22.51 Methabenzthiazuron 7.47 7.88 Metribuzin 1.10 0.93 1.75 1.85 1.99 2.33 1.15 1.55 2.26 1.29 Metsulfuron methyl 0.35 0.47 0.21 0.57 0.46 0.36 0.50 0.36 0.45 0.24 Napropamide 3.38 5.12 8.63 14.80 6.44 Paraquat 45.00 Pendimethalin 30.00 110.00 Phenmedipham 36.00 Propachlor 0.70 0.90 0.58 0.98 0.95 1.28 0.55 0.92 0.92 1.24 Propaquizafop 2.36 6.24 9.29 5.27 Prosulfocarb 11.70 24.70 32.80 Pyridate 0.37 2.30 0.30 Terbacil 0.51 0.83 0.57 0.28 1.18 0.59 0.69 0.24 0.00 0.63 Terbuthylazine 2.27 1.11 2.36 5.94 25.18 4.93 1.43 0.31 Thifensulfuron-methyl 0.21 0.21 0.21 0.20 0.22 0.20 0.20 0.20 0.20 0.20 Tri-allat 36.78 32.53 26.43 27.66 29.92 35.53 37.19 21.71 22.35 28.19 Triasulfuron 2.18 2.26 2.86 3.16 0.86 1.78 1.45 2.23 2.01 3.00 Tribenuron methyl 0.19 0.45 2.00 1.70 Trifluralin 96.52 178.32 146.82 116.06 197.50 157.25 130.72 128.95 127.88 122.75 Triflusulfuron methyl 0.36 0.50 1.28 0.41 0.67

43 Annex F. Kd Continued

Pesticide Insecticides alpha-cypermethrin 821 1042 868 Carbofuran 0.10 0.56 0.31 1.23 1.08 Cypermethrin 1014 1491 1910 Deltamethrin 24814 4413 22361 26663 12090 33724 28136 36790 21320 30123 Diazinon 8.97 7.49 9.32 11.40 9.19 9.04 10.79 9.15 9.30 11.77 Dimethoate 0.37 0.52 0.43 0.41 0.25 0.10 0.53 0.41 0.44 0.42 Endosulfan 169.74 51.00 72.86 200.01 130.14 169.52 265.84 236.93 133.84 241.13 Esfenvalerate 4.41 6.36 71.32 104.83 Fenitrothion 4.94 4.45 4.84 4.65 5.65 4.60 3.77 5.57 5.38 3.78 Lambda-cyhalothrin 1290 464 1470 5350 Malathion 0.83 1.23 1.76 2.47 1.60 Mercaptodimethur 2.61 Metaldehyd 0.45 0.30 0.31 0.26 0.47 0.27 0.59 0.33 0.52 0.43 Oxydemeton-methyl 0.18 0.34 0.39 0.05 0.27 0.39 0.29 0.15 0.36 0.31 Pirimicarb 0.19 6.05 5.20 7.98 3.40 4.04 1.70 3.84 5.34 0.00 Tau-fluvalinate 2614 8679 19668 17151 2540 16163 16853 13008 14732 23603

44 Annex H. Acute Toxicity. Fish96hrLC50 (mg l-1) Pesticide Fungicides Azoxystrobin 0.4700 2.8000 Benomyl 0.4100 0.1700 Carbenazim 0.3600 Chlorothalonil 0.0560 0.0560 0.0470 0.0600 0.0600 0.0550 0.0430 0.0870 0.0840 0.0320 0.1950 0.0440 Cyprodinil 0.9800 1.0400 1.3300 1.1700 1.1700 1.0300 1.2500 1.4000 1.0700 1.1000 1.4300 2.1700 2.4100 Fenpropidin 2.8900 2.5700 2.1600 1.9300 4.3800 3.6800 3.5500 1.9300 Fenpropimorph 9.5000 3.4300 3.2000 3.1600 Fluazinam 0.1700 0.1400 0.1100 0.1500 0.1500 Iprodion 3.1000 Kresoxim-methyl 3.2000 0.4140 0.1900 Mancozeb 1.5000 1.5000 1.5000 4.8000 4.4000 4.2000 2.5100 2.5100 2.5100 4.6000 Maneb 3.0500 0.4600 0.2200 Prochloraz 2.2000 2.2000 2.8000 Propamocarb 163.00 00 Propiconazole 20.000 6.4000 5.1000 5.1000 6.3000 6.3000 5.3000 6.8000 5.1000 6.8000 6.4000 3.1300 3.0100 2.6000 Propineb 0.5000 Pyrazophos 0.0160 Tebuconazol 6.4000 8.7000 6.1000 5.7000 4.4000 3.7400 Thiabendazol 0.5600 Thiophanat methyl 7.8000 Triadimenol 14.000 15.000 Tridemorph 3.5000 Growth regulators Chlormequat Chloride 465.00 Ethephon 720.00 720.00 311.00 357.00 300.00 Maleinhydrazid 1608.2 Mepiquat-chlorid 3300.0 3300.0 3500.0 2500.0 5036.0 4320.0 Trinexapac-ethyl 92.000 82.400 68.000 57.000 38.000 36.000 35.000 32.500 Herbicides 2.4-D 1.1000 Aclonifen 1.2000 1.1000 0.6700 1.5000 1.4000 1.3000 0.6250 Asulam 3000.0 Atrazin 4.3000 Benazolin 2.8000 27.000 Bentazon 255.00 1500.0 780.00 180.00 Bromoxynil 0.0600 23.000 Carbetamid 354.00 Chloridazon 20.000 Clopyralid 103.50 125.40 500.00 Cyaniazin 4.8000

45 Annex H. Fish96hrLC50 Continued Pesticide Herbicides (continued) Desmedipham 1.7000 6.3000 6.0000 Dicamba 135.00 Dichlorprop-P 428.00 100.00 Difenzoquat methyl sulfat 891.90 788.00 696.00 694.00 Diflufenican 77.000 75.000 70.000 Diquat 27.000 23.000 21.000 143.00 91.000 67.000 6.1000 Ethofumesate 25.000 64.100 11.900 12.650 12.400 14.510 17.350 15.000 Fenoxaprop-P-ethyl 4.2000 4.2000 1.5000 1.3000 0.6100 0.5800 0.5800 0.6900 0.5700 0.5200 0.5000 0.4600 Flamprop-M-isopropyl 2.4000 Fluazifop-P-butyl 1.4500 1.3700 1.3700 1.3200 1.3100 1.3100 117.00 1.0700 Fluroxypyr 14.300 24.500 4.6000 4.2000 3.5000 Glufosinate ammonium 39.900 26.700 28.100 320.00 Glyphosate 38.000 Glyphosate-Trimesium 1800.0 3100.0 4800.0 664.00 541.00 517.00 441.00 441.00 441.00 Haloxyfop 0.0035 548.00 0.2900 0.5380 0.2900 0.2900 0.2840 0.7430 0.6590 0.6380 1.6700 1.3400 1.1800 ethoxyethylester Ioxynil 0.7500 0.6400 9.5000 8.5000 8.4000 Isoproturon 9.0000 Linuron 16.200 30.600 16.400 0.8900 MCPA 748.00 97.000 MCPB 5.6000 Mechlorprop-P 100.00 Metamitron 326.00 443.00 222.00 222.00 225.00 Methabenzthiazuron 15.900 29.000 48.700 20.000 Metribuzin 80.000 131.00 76.000 147.00 85.000 64.000 59.000 Metsulfuron methyl 981.00 Napropamide 32.000 30.000 15.000 13.500 16.600 10.700 Paraquat 2.5000 Pendimethalin 0.1400 0.1380 1.0000 0.5200 86.600 72.440 Phenmedipham 3.0000 1.4100 3.9800 Propachlor 0.1700 Propaquizafop 1.1880 0.2000 0.1900 0.1900 0.4500 0.3700 0.3400 Propyzamid 350.00 150.00 72.000 Prosulfocarb 3.7000 3.0000 4.2000 7.0000 1.8600 1.6500 1.6500 Pyridate 81.000 48.000 145.00 124.00 118.00 20.000 12.000 10.000 Terbacil 46.000 Terbuthylazine 3.8000 3.8000 7.0000 7.0000 6.2000 19.000 15.000 55.000 15.000 24.800 26.000 29.000 18.000 18.000 19.000 3.3000 3.4000 3.9000 9.4000 4.6000 Tri-allat 1,2000 1,3000 Trifluralin 0.0100 0.0200 0.1900 0.6600 0.0450 0.4170 2.2000 0.2100 0.1520 0.0420 0.0100 0.0860 0.0410 0.1050 0.1050 0.6730 0.0540 0.0166 Triflusulfuron methyl 530.00 420.00 150.00 730.00 860.00 750.00 870.00 760.00 760.00

46 Annex H. Fish96hrLC50 Continued Pesticide Insecticides alpha-cypermethrin 0.0056 0.3500 0.0650 0.1200 0.2200 0.0028 0.0009 0.0010 0.0011 0.0008 0.0110 0.0600 0.0004 0.0032 0.0220 0.011 0.0008 0.0028 0.24 5 Carbofuran 0.2800 0.2100 0.2400 0.0073 Chlorfenvinphos 0.5500 0.9000 0.7700 0.0570 0.0510 0.0390 Cypermethrin 0.0011 0.0010 0.0009 0.0022 0.0019 0.0018 0.0023 0.0020 0.0016 0.0007 0.0007 0.0007 0.0009 0.0011 0.0099 0.011 0.0008 0.0021 0.0091 0.0005 0.0071 0.0028 0.24 0.005 0.0047 0.0009 0.0133 0.013 0.0009 0.0012 Deltamethrin 0.0006 Diazinon 0.2100 Dimethoate 30.200 694.00 6.2000 Endosulfan 0.0020 Esfenvalerate 0.0003 0.0013 0.0013 0.0012 0.0028 0.0028 0.0027 0.0018 0.0018 0.0018 0.0056 0.0056 0.0056 0.0019 0.0019 0.0019 0.0046 0.0045 0.0044 0.0156 0.0003 Fenitrothion 1.700 Lambda-cyhalothrin 0.0002 0.0005 0.0112 0.0002 Malathion 0.0400 Mercaptodimethur 0.44 Metaldehyd 7.3000 Mevinphos 0.0120 Oxydemeton-methyl 1.9000 Phosphamidon 1.4100 3.2000 00048 Phoxim 0.81 0.2200 Pirimicarb 40.000 32.000 180.00 124.00 60.000 36.000 380.00 315.00 156.00 141.00 129.00 132.00 95.000 78.000 34.000 30.000 29.000 130.00 89.000 55.000 Tau-fluvalinate 0.0053 0.0043 0.0042 0.0110 0.0110 0.0110 0.0032 0.0028 0.0027 0.0075 0.0070 0.0062

47 Annex I. Acute Toxicity. Daphnia 48hrEC50(mg l-1) Pesticide Fungicides Azoxystrobin 0.2800 0.1100 Benomyl 0.5500 Carbenazim 0.1300 Chlorothalonil 0.0700 Cyprodinil 0.1000 0.3900 Fenpropidin 3.3000 0.5400 Fenpropimorph 3.5000 2.3900 Fluazinam 0.3600 0.1900 Iprodion 0.2500 Kresoxim-methyl 0.1860 1.5100 0.6000 0.3500 Mancozeb 13.6000 13.6000 Maneb 3.4000 1.0200 0.5200 Metalaxyl 610.0000 Prochloraz 2.6000 Propamocarb 284.0000 Propiconazole 11.5000 1.7000 Propineb 4.7000 Pyrazophos 0.0002 Tebuconazol 11.8000 5.9000 3.2000 Thiabendazol 0.4500 Thiophanat methyl 12.7000 Triadimenol 2.5000 Tridemorph 1.3000 Vinclozolin 4.0000 Growth regulators Chlormequat Chloride 59.0000 16.0000 16.9000 100.0000 7.4000 Ethephon 31.7000 78.0900 78.0900 Mepiquat-chlorid 68.5000 Trinexapac-ethyl 142.0000 Herbicides 2.4-D 1.4000 Aclonifen 2.0000 1.2000 2.5000 Asulam 63.4000 Atrazin 3.6000 Benazolin 233.4000 Bentazon 170.7600 125.0000 Bromoxynil 21.0000 12.5000 Carbetamid 54.0000 Chloridazon 50.1000 Chlorsulforon 370.0000 Clopyralid 225.0000 Cyaniazin 42.0000 Desmedipham 0.5900 Dicamba 400.0000 110.0000 Dichlorprop-P 1300.0000 Difenzoquat methyl sulfat 20.0000 44.0000 Diquat 2.5000 Ethofumesate 34.0000 22.0000 33.0000 295.0000 Fenoxaprop-P-ethyl 2.7000 Flamprop-M-isopropyl 10.0000 18.6000 Fluazifop-P-butyl 3.0000 2.1000 5.8000 6.1000 Fluroxypyr 3.8000 1.1000 1.2000 Glufosinate ammonium 560.0000 Glyphosate 2.2500 84.0000 281.0000 Glyphosate-Trimesium 27.0000 4.2000 12.0000 Haloxyfop ethoxyethylester 4.6400 Ioxynil 5.6000 3.9000 Isoproturon 507.0000 Isoxaben 544.0000 Linuron 0.1200 MCPA 123.0000 78.0000 160.0000 Mechlorprop-P 420.0000 Metamitron 101.7000 167.6000 111.9000 Methabenzthiazuron 30.6000 Metribuzin 100.0000 35.0000 4.5000 Metsulfuron methyl 971.0000 Napropamide 14.3000 Pendimethalin 0.0400 Phenmedipham 3.2000

48 Annex I. Daphnia 48hrEC50 continued Pesticide Herbicides (continued) Propachlor 7.8000 Prosulfocarb 1.3000 Pyridate 0.8300 Terbacil 68.0000 Terbuthylazine 21.2000 Thifensulfuron-methyl 970.0000 Tri-allat 0.4300 Tribenuron methyl 720.0000 Trifluralin 0.2400 Triflusulfuron methyl 1200.0000 280.0000 460.0000 490.0000 Insecticides alpha-cypermethrin 0.0011 0.0003 Carbofuran 0.0150 Chlorfenvinphos 0.0012 0.0003 0.0003 Cypermethrin 0.0012 0.0002 Deltamethrin 0.0035 Diazinon 0.9900 Dimethoate 0.4600 Endosulfan 0.0750 Esfenvalerate 0.0009 0.0008 0.0010 0.0001 0.0002 Fenitrothion 0.0016 Malathion 0.0022 Mevinphos 0.0002 Oxydemeton-methyl 0.1100 Phosphamidon 0.0200 Phoxim 0.0006 Pirimicarb 0.0220 0.0190 0.0190 0.0140

49 Annex J. Acute & Chronic Toxicity. Algae96hrEC50

Pesticide Fungicides Azoxystrobin 0.0540 Benomyl 2.0000 Carbenazim 1.3000 Chlorothalonil 0.5210 0.5250 0.2100 Cyprodinil 0.7500 1.9700 1.7500 1.7800 2.8400 2.2200 2.1100 2.2500 3.8100 3.7600 2.8900 2.8200 2.6000 Fenpropidin 0.0025 4.4000 0.0057 Fenpropimorph 0.1700 2.2100 0.2900 Fluazinam 0.1600 Iprodion 0.0480 1.9 Kresoxim-methyl 0.0630 0.0710 0.5320 Mancozeb 2.8000 0.0110 1.1600 2.2000 1.1000 Maneb 0.2610 0.4300 Metalaxyl 42.0000 Prochloraz 0.0730 Propamocarb 301.000 Propiconazole 0.0034 0.6800 0.0008 6.5000 2.2000 1.0000 6.3000 5.0000 1.5000 0.7200 1.6000 Propineb 0.4900 Pyrazophos 65.5000 Tebuconazol 1.9600 1.6400 5.3000 4.0100 Thiabendazol 9.0000 Triadimenol 3.7000 Tridemorph 0.2600 Vinclozolin 10.0000 16.0000 Growth regulators Chlormequat Chloride 5656.00 5747.00 1998.00 Ethephon 29.0000 32.0000 16.5000 25.0000 21.0000 Maleinhydrazid 8.0000 Mepiquat-chlorid 267.984 Trinexapac-ethyl 0.4000 0.7300 9.4000 12.0000 21.0000 16.0000 18.0000 19.0000 55.0000 26.0000 11.0000 Herbicides 2.4-D 243.682 48.0358 385.415 256.257 346.703 2.7963 382.763 17.1773 301.917 90.5628 Aclonifen 0.0067 0.0069 0.0290 Asulam 11.0000 13.5000 Atrazin 0.0440 Benazolin 16.0000 Bentazon 47.3580 279.262 34.8000 Bromoxynil 1.7700 44.0000 Carbetamid 210.000 Chloridazon 1.9000 Clopyralid 6.9000 7.3000 730.000 449.000

50 Annex J. Algae96hrEC50. Continued. Pesticide Herbicides (continued) Diquat 0.0110 0.0190 Ethofumesate 3.9000 0.0600 Fenoxaprop-P-ethyl 1.3000 0.5100 Flamprop-M-isopropyl 6.8000 Fluroxypyr 3.8500 3.8000 49.8000 Glufosinate ammonium 1000.00 Glyphosate 326.900 117.800 485.000 1.3000 166.000 72.9000 1.0000 Glyphosate-Trimesium 19.0000 14.0000 30.0000 Haloxyfop ethoxyethylester 106.500 80.7200 Ioxynil 24.0000 10.0000 Isoproturon 0.0300 0.0225 0.0210 0.0135 0.0770 Isoxaben 60.3300 60.2100 MCPA 115.000 220.000 Mechlorprop-P 270.000 220.000 Metamitron 0.2200 Methabenzthiazuron 0.0420 0.1190 Metribuzin 0.0078 0.0069 0.0220 0.0210 0.0430 Metsulfuron methyl 64.0000 63.0000 62.0000 73.0000 70.0000 69.0000 2.9000 Napropamide 4.5000 Pendimethalin 0.0550 1.6000 5.0700 0.4677 5.8200 0.0220 0.0054 15.4000 72.4400 Phenmedipham 0.1302 Propyzamid 5.8000 2.9000 Prosulfocarb 0.1090 202.000 0.0900 Pyridate 82.0000 Terbacil 0.3000 Terbuthylazine 0.0200 0.0390 0.0730 0.0160 0.0190 0.0460 Thifensulfuron-methyl 14.5000 15.0000 Tri-allat 0.1200 Triasulfuron 0.7700 Tribenuron methyl 7.0000 4.5000 Trifluralin 0.0850 Triflusulfuron methyl 0.5000 0.6200

51 Annex J. Algae96hrEC50. Continued. Pesticide Insecticides Carbofuran 7.0000 19.9000 Chlorfenvinphos 1.6000 Diazinon 6.4000 Dimethoate 282.290 90.4300 Endosulfan 1.4400 Esfenvalerate 1.0000 Fenitrothion 3.9000 Lambda-cyhalothrin 27.0000 31.0000 Malathion 100.000 1.0000 Mercaptodimethur 1.2000 Metaldehyd 73.0000 Oxydemeton-methyl 100.000 Phosphamidon 240.000 Phoxim 0.6500 Pirimicarb 140.000 180.000

52 Annex K. Chronic Toxicity. Fish21dayNOEC Pesticide Fungicides Azoxystrobin 0.1470 Cyprodinil 0.0830 Fenpropidin 0.3200 Fenpropimorph 0.1000 Kresoxim-methyl 0.0200 Growth regulator Chlormequat Chloride 1000000 Herbicides Aclonifen 0.0100 Desmedipham 16000 Dicamba 1800000 Dichlorprop-P 1000000 Diflufenican 0.0500 Diquat 14000 Fluazifop-P-butyl 0.2500 Fluroxypyr 0.3200 0.5600 Glyphosate 520000 Glyphosate-Trimesium 1800000 MCPA 800000 Mechlorprop-P 500000 Metribuzin 56000 Propaquizafop 0.0710 Terbuthylazine 0.2380 Triflusulfuron methyl 1000000 2100000 Insecticide Dimethoate 0.4000

53 Annex L. Chronic Toxicity. Daphnia 21day NOEC Pesticide Fungicides Azoxystrobin 0.0440 Chlorothalonil 0.1800 Fenpropimorph 0.0710 Fluazinam 0.0125 Kresoxim-methyl 0.0320 0.1500 Mancozeb 0.0290 Prochloraz 0.0222 Propamocarb 88900 Growth regulators Chlormequat Chloride 156000 Ethephon 156000 625000 670000 0.0980 Herbicides Desmedipham 0.0100 Dichlorprop-P 1000000 Diflufenican 0.1630 Diquat 0.1250 Ethofumesate 63200 0.6400 0.3200 Fenoxaprop-P-ethyl 0.1000 0.2200 Flamprop-M-isopropyl 0.8370 0.1670 Fluazifop-P-butyl 0.2500 Fluroxypyr 0.1000 560000 Glyphosate 300000 1000000 Isoxaben 0.6900 MCPA 500000 500000 Mechlorprop-P 500000 Metamitron 320000 320000 Methabenzthiazuron 20000 Metribuzin 15000 Phenmedipham 0.0320 Propaquizafop 0.4400 Prosulfocarb 0.0470 Terbuthylazine 0.2100 Thifensulfuron-methyl 1000000 Triflusulfuron methyl 320000 110000 Insecticides alpha-cypermethrin 0.00003 Carbofuran 0.00980 0.00020 Dimethoate 0.04000 Esfenvalerate 0.00000 Pirimicarb 0.00200 0.00100 Tau-fluvalinate 0.00010

54 Annex L. Chronic Toxicity. Daphnia 21day NOEC Pesticide alpha-cypermethrin 0.0000 Azoxystrobin 0.0440 Carbofuran 0.0098 0.0002 Chlormequat Chloride 15.6000 Chlorothalonil 0.1800 Desmedipham 0.0100 Dichlorprop-P 100.0000 Diflufenican 0.1630 Dimethoate 0.0400 Diquat 0.1250 Esfenvalerate 0.0000 Ethephon 15.6000 62.5000 67.0000 0.0980 Ethofumesate 6.3200 0.6400 0.3200 Fenoxaprop-P-ethyl 0.1000 0.2200 Fenpropimorph 0.0710 Flamprop-M-isopropyl 0.8370 0.1670 Fluazifop-P-butyl 0.2500 Fluazinam 0.0125 Fluroxypyr 0.1000 56.0000 Glyphosate 30.0000 100.0000 Isoxaben 0.6900 Kresoxim-methyl 0.0320 0.1500 Mancozeb 0.0290 MCPA 50.0000 50.0000 Mechlorprop-P 50.0000 Metamitron 32.0000 32.0000 Methabenzthiazuron 2.0000 Metribuzin 1.5000 Phenmedipham 0.0320 Pirimicarb 0.0020 0.0010 Prochloraz 0.0222 Propamocarb 8.8900 Propaquizafop 0.4400 Prosulfocarb 0.0470 Tau-fluvalinate 0.0001 Terbuthylazine 0.2100 Thifensulfuron-methyl 100.0000 Triflusulfuron methyl 32.0000 11.0000

55 Annex M. Fraction of missing data for acute indicators. Fractions were calculated in relation to total area treated. (Acute toxicity, Kd and DT50)

Year LI REXTOX ADSCOR Fish Daph Algae Fish Daph Algae Fish Daph Algae 1992 0.008 0.016 0.018 0.019 0.024 0.026 0.008 0.016 0.018 1993 0.005 0.011 0.011 0.009 0.016 0.016 0.005 0.011 0.011 1994 0.003 0.009 0.008 0.006 0.013 0.013 0.003 0.009 0.008 1995 0.003 0.008 0.009 0.006 0.012 0.013 0.003 0.008 0.009 1996 0.002 0.007 0.007 0.003 0.008 0.008 0.002 0.007 0.007 1997 0.003 0.006 0.007 0.004 0.007 0.008 0.004 0.006 0.007 1998 0.002 0.008 0.006 0.002 0.008 0.006 0.002 0.008 0.006 1999 0.002 0.014 0.008 0.003 0.015 0.009 0.002 0.014 0.008 2000 0.003 0.010 0.007 0.003 0.010 0.008 0.002 0.009 0.006

Annex N. Fraction of missing data for acute indicators. Fractions were calculated in relation to total area treated. Fractions identical across indicators because data on chronic toxicity sets the lower limit. (Chronic toxicity, Kd and DT50).

Year LI REXTOX ADSCOR Fish Daph Algae Fish Daph Algae Fish Daph Algae 1992 0.483 0.656 0.358 0.483 0.656 0.358 0.483 0.656 0.358 1993 0.513 0.658 0.403 0.513 0.658 0.403 0.513 0.658 0.403 1994 0.513 0.635 0.401 0.513 0.635 0.401 0.513 0.635 0.401 1995 0.517 0.662 0.409 0.517 0.662 0.409 0.517 0.662 0.409 1996 0.450 0.626 0.361 0.450 0.626 0.361 0.450 0.626 0.361 1997 0.428 0.563 0.429 0.428 0.563 0.429 0.428 0.563 0.429 1998 0.441 0.587 0.455 0.441 0.587 0.455 0.441 0.587 0.455 1999 0.528 0.678 0.516 0.528 0.678 0.516 0.528 0.678 0.516 2000 0.549 0.733 0.527 0.549 0.733 0.527 0.549 0.733 0.527

56 Annex O. Example of random selection of input data of acute fish toxicity. Example is calculated for Alpha-cypermethrin and illustrates how the selection affects LI. The toxicity values (mg/l) is listed in Appendix E and the usage data is seen in Appendix C. The LI indicator is defined by Eq. 2.2. A large variation in the magnitude of 105 mainly due to the variability of the TOX values.

Year: 1992 1993 1994 1995 1996 1997 1998 1999 2000 Total usage (kg): 3115 3146 1263 4722 1303 609 659 1287 602 AGRAyea (1000 2532 2351 2272 2289 2319 2326 2321 2233 2188 ha): TOX Fish96hrLC50 (mg/l) 0.0056 0.21969 0.23896 0.09927 0.36838 0.10034 0.04675 0.05070 0.10292 0.04913 0.3500 0.00352 0.00382 0.00159 0.00589 0.00161 0.00075 0.00081 0.00165 0.00079 0.0650 0.01893 0.02059 0.00855 0.03174 0.00864 0.00403 0.00437 0.00887 0.00423 0.1200 0.01025 0.01115 0.00463 0.01719 0.00468 0.00218 0.00237 0.00480 0.00229 0.2200 0.00559 0.00608 0.00253 0.00938 0.00255 0.00119 0.00129 0.00262 0.00125 0.0028 0.43938 0.47791 0.19853 0.73675 0.20067 0.09351 0.10140 0.20584 0.09826 0.0009 1.32285 1.43888 0.59774 2.21818 0.60417 0.28153 0.30530 0.61974 0.29585 0.0010 1.23025 1.33815 0.55590 2.06291 0.56188 0.26182 0.28393 0.57635 0.27514 0.0011 1.11841 1.21650 0.50536 1.87537 0.51080 0.23802 0.25812 0.52396 0.25012 0.0008 1.53782 1.67269 0.69487 2.57864 0.70235 0.32728 0.35491 0.72044 0.34392 0.0110 0.11184 0.12165 0.05054 0.18754 0.05108 0.02380 0.02581 0.05240 0.02501 0.0600 0.02050 0.02230 0.00926 0.03438 0.00936 0.00436 0.00473 0.00961 0.00459 0.0004 3.07563 3.34538 1.38974 5.15727 1.40470 0.65456 0.70982 1.44089 0.68784 0.0032 0.38445 0.41817 0.17372 0.64466 0.17559 0.08182 0.08873 0.18011 0.08598 0.0220 0.05592 0.06083 0.02527 0.09377 0.02554 0.01190 0.01291 0.02620 0.01251 0.011 0.11184 0.12165 0.05054 0.18754 0.05108 0.02380 0.02581 0.05240 0.02501 0.0008 1.53782 1.67269 0.69487 2.57864 0.70235 0.32728 0.35491 0.72044 0.34392 0.0028 0.43938 0.47791 0.19853 0.73675 0.20067 0.09351 0.10140 0.20584 0.09826 0.24 0.00513 0.00558 0.00232 0.00860 0.00234 0.00109 0.00118 0.00240 0.00115 5 0.00025 0.00027 0.00011 0.00041 0.00011 0.00005 0.00006 0.00012 0.00006

57 Annex P. Min - Max risk indices for fungicides Calculated using the minimum and maximum possible input values for toxicity and fate. Notice that indices have been scaled to allow comparisons within indices but not between different indices. 3.5 6.0 LI Fish Rextox Fish 3.5 3.0 Min 5.0 3.0 ADSCOR Fish 2.5 Max 4.0 2.5 2.0 3.0 2.0 1.5 1.5 2.0 1.0 1.0 0.5 1.0 0.5 Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled 0.0 value indicator Scaled 0.0 0.0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000

3.0 3.5 3.0 LI Daphnia ADSCOR Daphnia 2.5 3.0 REXTOX Daphnia 2.0 2.5 2.0 2.0 1.5 1.5 1.0 1.0 1.0 0.5 0.5 Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled 0.0 0.0 value indicator Scaled 0.0 1992 1994 1996 1998 20001992 1994 1996 1998 2000 1992 1994 1996 1998 2000

3.0 3.5 7.0 LI algae REXTOX algae ADSCOR algae 2.5 3.0 6.0 2.5 5.0 2.0 2.0 4.0 1.5 1.5 3.0 1.0 1.0 2.0 0.5 0.5 1.0 Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled 0.0 0.0 0.0 1992 1994 1996 1998 20001992 1994 1996 1998 2000 1992 1994 1996 1998 2000

58 Annex Q. Min - Max risk indices for growth regulators Calculated using the minimum and maximum possible input values for toxicity and fate. Notice that indices have been scaled to allow comparisons within indices but not between different indices. 2.0 2.5 1.8 LI Fish 2.0 1.5 Min 1.4 Max 1.5 1.0 1.0 1.0 REXTOX Fish 0.6 ADSCOR Fish 0.5 0.5 0.2 Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled Scaled indicator value 0.0 0.0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000

3.5 4.0 LI Daphnia 5.0 3.0 ADSCOR Daphnia 4.0 2.5 REXTOX Daphnia 3.0 2.0 3.0 1.5 2.0 2.0 1.0 1.0 0.5 1.0 Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled 0.0 value indicator Scaled 0.0 0.0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000

7.0 12.0 6.0 ADSCOR algae LI algae 3.0 REXTOX algae 5.0 10.0 4.0 8.0 2.0 3.0 6.0 2.0 4.0 1.0 1.0

Scaled indicator value indicator Scaled 2.0 Scaled indicator value

0.0 value indicator Scaled 1992 1994 1996 1998 2000 0.0 0.0 1992 1994 199659 1998 2000 1992 1994 1996 1998 2000 Annex R. Min - Max risk indices for herbicides Calculated using the minimum and maximum possible input values for toxicity and fate. Notice that indices have been scaled to allow comparisons within indices but not between different indices. 4.0 4.0 LI Fish REXTOX Fish ADSCOR Fish 3.0 3.0 3.0 2.0 2.0 2.0

1.0 Min 1.0 1.0 Max Scaled indicator value Scaled indicator value

Scaled indicator value 0.0 0.0 0.0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000

REXTOX Daphnia 2.0 1.6 LI Daphnia 1.6 ADSCOR Daphnia 1.2 1.2

0.8 0.8 1.0

0.4 0.4 Scaled indicator value indicator Scaled Scaled indicator value 0.0 0.0 value indicator Scaled 0.0 1992 1994 1996 1998 20001992 1994 1996 1998 20001992 1994 1996 1998 2000

3.0 3.0 LI algae REXTOX algae 2.5 2.5 2.5 ADSCOR algae 2.0 2.0 2.0 1.5 1.5 1.5 1.0 1.0 1.0 0.5 0.5 0.5 Scaled indicator value indicator Scaled 0.0 value indicator Scaled 0.0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 value indicator Scaled 0.0 1992 1994 1996 1998 2000 60 Annex S. Min - Max risk indices for insecticides Calculated using the minimum and maximum possible input values for toxicity and fate. Notice that indices have been scaled to allow comparisons within indices but not between different indices.

5,0 6,0 LI Fish REXTOX Fish 4,0 ADSCOR Fish 4,0 5,0 4,0 3,0 3,0 Min 3,0 Max 2,0 2,0 2,0 1,0 1,0 1,0 Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled 0,0 value indicator Scaled 0,0 0,0 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000

6.0 5.0 4.0 LI Daphnia REXTOX Daphnia ADSCOR Daphnia 5.0 4.0 3.0 4.0 3.0 2.0 3.0 2.0 2.0 1.0 1.0 1.0 0.0 Scaled indicator value indicator Scaled Scaled indicator value indicator Scaled 0.0 0.0

Scaled indicator value indicator Scaled 1992 1994 1996 1998 2000 1992 1994 1996 1998 20001992 1994 1996 1998 2000

2.5 LI algae 4.0 2.5 ADSCOR algae 2.0 REXTOX algae 2.0 3.0 1.5 1.5 1.0 2.0 1.0 0.5 1.0 0.5

Scaled indicator value indicator Scaled 0.0 0.0 value indicator Scaled 0.0 1992 1994 1996 1998 2000 value indicator Scaled 1992 1994 1996 1998 2000 1992 1994 1996 1998 2000 61 Annex T. Scaled risk indices for Insecticides & Herbicides Scaled risk indices for Fish, Daphnia and Algae calculated for insecticides and herbicides. Calculations were based on averaged input values. Indices have been scaled to allow comparisons within indices but not between different indices.

2.5 Fish FT 3 2.5 Algae 2 LI Daphnia 2.5 REXTOX 2 1.5 2 ADSCOR 1.5 1 1.5 1 1 0.5 0.5 0.5 0 1992 1994 1996 1998 20000 0 1992 1994 1996 1998 20001992 1994 1996 1998 2000 Insecticides

2.5 1.8 Fish FA Daphnia Algae 1.4 2 LI 1.4 REXTOX 1.5 1 ADSCOR 1 1 0.6 0.6 0.5 0.2 0.2 0 0 0 1992 1994 1996 1998 20001992 1994 1996 1998 20001992 1994 1996 1998 2000 Herbicides

62 Annex U. Scaled risk indices for Fungicides & Growth regulators Scaled risk indices for Fish, Daphnia and Algae calculated for fungicides and growth regulators. Calculations were based on averaged input values. Indices have been scaled to allow comparisons within indices but not between different indices.

3 3.5 3 FA Daphnia Fish 3 Algae 2.5 LI 2.5 2.5 2 REXTOX 2 2 1.5 ADSCOR 1.5 1.5 1 1 1 0.5 0.5 0.5 0 0 0 1992 1994 1996 1998 20001992 1994 1996 1998 20001992 1994 1996 1998 2000 Fungicides

2.5 3 Fish FA Daphnia 4 Algae 2 LI 2.5 REXTOX 2 3 1.5 ADSCOR 1.5 1 2 1 0.5 0.5 1

0 0 0 1992 1994 1996 1998 2000 1992 1994 1996 1998 20001992 1994 1996 1998 2000 Growth redulators Growth

63 Annex X. Indicator agreement on trends. Analysis includes every type of pesticide. Increasing values (+) and decreasing values (-) between progressing pair of years. FA LI REXTOX ADSCOR ∑ Years Fish Daphni Algae Fish Daphni Algae Fish Daphni Algae + - Fungicides 1992 1993 - - + - - + - - - - 2 8 1993 1994 - - + - - + - + + - 4 6 1994 1995 + - - + - - + - - - 3 7 1995 1996 ------+ 1 9 1996 1997 + + + + - - - + + - 6 4 1997 1998 + + + - + + + + + + 9 1 1998 1999 + - + + - + - - + + 6 4 1999 2000 ------0 10 Growth regulators 1992 1993 - + + - + + - - - - 4 6 1993 1994 ------0 10 1994 1995 + + + + + + + + + + 10 0 1995 1996 ------0 10 1996 1997 + + + + + + + + + + 10 0 1997 1998 + + + + + + + + + + 10 0 1998 1999 + + + - - + - - + - 5 5 1999 2000 ------0 10 Herbicides 1992 1993 - + + + - - - + - - 4 6 1993 1994 - + + + + + + + - + 8 2 1994 1995 + + + + + + + + + - 9 1 1995 1996 - - + + - + + - + + 6 4 1996 1997 + + + + + + + + + + 10 0 1997 1998 + - + - - + - - + - 4 6 1998 1999 + ------1 9 1999 2000 - + + - - + - + + - 5 5 Insecticides 1992 1993 - + + - + - - - - - 3 7 1993 1994 ------+ + 2 8 1994 1995 + + + + + + + + + + 10 0 1995 1996 ------0 10 1996 1997 + + + + + + + + + + 10 0 1997 1998 + ------1 9 1998 1999 + + + + - - - - + + 6 4 1999 2000 ------0 10

64 Annex Y. Trend analysis for Indicators during the period 1992-2000. Calculated indicators for the different years were log transformed causing indicators to be normally distributed. Residuals were then calculated by subtracting from each year’s value the corresponding average value for the period 1992-2000. Residuals were subsequently tested for temporal trends using Kendall’s τ. FA calculated for all pesticides. Significant trends are shown in bold. Pesticide Indicator Kendall’s τ Probability All groups FA -0.556 0.0371 Insecticides Fish LI -0.444 0.0953 REXTOX -0.722 0.0067 ADSCOR -0.500 0.0035 Daphnia LI -0.500 0.0606 REXTOX -0.778 0.0035 ADSCOR -0.611 0.0218 Algae LI 0.667 0.0123 REXTOX -0.833 0.0018 ADSCOR -0.611 0.0218 Fish Herbicides LI 0.056 0.8348 REXTOX -0.722 0.0067 ADSCOR 0 1 Daphnia LI 0.611 0.0218 REXTOX 0.444 0.0953 ADSCOR 0.389 0.1444 Algae LI 0 1 REXTOX -0.333 0.2109 ADSCOR -0.500 0.0606 Fungicides Fish LI -0.722 0.0067 REXTOX -0.778 0.0035 ADSCOR -0.167 0.5316 Daphnia LI -0.222 0.4042 REXTOX -0.278 0.2971 ADSCOR -0.278 0.2971 Algae LI -0.333 0.2109 REXTOX -0.722 0.0067 ADSCOR -0.722 0.0067 Growth Fish regulators LI -0.333 0.2109 REXTOX -0.500 0.0606 ADSCOR -0.500 0.0606 Daphnia LI -0.278 0.2971 REXTOX -0.389 0.1444 ADSCOR -0.500 0.0606 Algae LI 0.056 0.8348 REXTOX -0.056 0.8348 ADSCOR 0.056 0.8348

65 Annex Z. Exposure estimation of REXTOX in field studies Measured and estimated pesticide concentration in streams in connection with spray drift and surface run-off events. Predicted concentrations in streams were calculated according to REXTOX using available information on depth of stream, characteristics of pesticide (T½, Koc), application (dose, interval), water index, area treated, slope, precipitation and buffer width. In case of missing information slope was assumed to 2.3 %, buffer zone set to 0 and soil assumed to be sandy (carbon % of 1.3), covered with vegetation and dry. Pesticide Pesticide conc. in stream Precipita. Stream Q Remark Ref. (mm d-1) (m3 s-1) Spray-drift Run-off Predicted (µg l-1) (µg l-1) (REXTOX) Azinphos-M 0.04±0.01 (within 95% of 00.28Orchard Schulz 2001 predicted) Arial spray Azinphos-M 0.26 0.15 6.8 7.5 Azinphos-M 1.5 1.4 28.8 22.4 Endosulfan 0.07±0.02 (within 95% of 0 predicted) Endosulfan 0.13 0.003 6.8 7.5 Endosulfan 2.9 0.06 28.8 22.4 Fenvalerate - 6.2 0.30 15 0.02 Field spray Liess et al. Parathion - 6.0 1.7 8 0.03 Carbofuran -26 2.6 >15 - Granules; drain Mathiessen et al. 1995 run-off + drainage conc. 264 µg/l Cypermethrin 0.03 0.01-0.09 Field spray Shires & Bennett 1985 Fenvalerate 0.11 0.01-0.04 Field spray Baughman et al. 1989

66 Annex AA. Effect of buffer zone width on spray drift and run-off Effect of buffer zone width on spray drift (% of dose applied on field) and run-off (% reduction through buffer zone) to streams (and ditch). For spray drift calculated percentages of field dose (according to REXTOX) are shown in brackets. For run-off calculated reductions according to REXTOX are shown in brackets. 0 m 1 m 2m 3 m 5 m 6 m 10 m 20 m Reference Spray drift Ditch bank 4-25 0-0.08 0 De Snoo & de (-) (0.82) (0.38) Wit Ditch 0.6-2.2 0-0.07 0 (-) (0.82) (0.38)

Run off Lindane 0 72 (68) 100 Gril et al. 1997 (96) Atrazine 0 44 (68) 100 (96) Metribuzin 0 51 (31) Webster & Shaw 1996 Norflurazon050 Murphy & Shaw (25)* 1998 Fluometuron 0 48 (24)* * seasonal average for 0.5 and 1 m buffer width

67 Annex BB. Modelling relation between FA and Pesticide Effects on Aquatic life. The effect of different Frequency of Treatment of pesticides in Danish agriculture on non-target organisms in ponds has previously been evaluated using a dynamic exposure-effect model (Møhlenberg & Gustavsson 1998). Briefly, the model considers wind drift and surface run-off from a “standard” field surrounding a small pond. Depending on the crop various pesticides are applied according to recommended date of application and doses. Wind drift is calculated according to Ganzelmeier functions taking account of buffer zones required for risk mitigation for the different insecticides. Pesticides on the field and vegetation are degraded according to published DT50 and modified by a temperature function. Surface run-off occurs only in case of heavy rainfall (>10 mm during 3 h) that is modelled by 3 Monte-Carlo functions to simulate actual rainfall during different seasons. During run-off events 0.2 % of pesticides remaining (after degradation) on 2 ha closest to the pond are transferred to the pond. In the pond pesticides dissipates due to degradation and sedimentation and affects populations of plankton algae, daphnia and macrophytes that are modelled using generic population models. Only direct (mortality) effects are considered using concentration-mortality relations obtained from literature. Effect of pesticides are calculated as deviations from control run (without pesticide application) in average biomass/numbers of plankton algae, daphnia and macrophytes during the growth season. Each field/pesticide combination was run 40-50 times, population biomass averaged and compared to control. Data was the averaged for different crops according to their relative coverage to arrive at an overall impact (Table V).

Table V. Probabilities of detecting significant effects on algae and daphnia at different Load Index. Data based on deterministic exposure-effect model. See text for further details.

Application Scenario Probability of significant Probability of significant effects (>10 % reduction) effects (> 10 % on algae reduction) on daphnia FT = 2.34 (Year 1997) 1) 85 % 55 % FT = 1.17 2) 45 % 25 % FT = 0.59 3) 20 % 15 % FT = 0.40 4) 10 % 10 % 1. Actual average application rate in 1997 based on distribution of crops and using pesticides at recommended frequency and dose. 2. Actual average application rate in 1997 divided by 2 (i.e. halving the frequency but maintaining doses). 3. Only grain and grass grown and pesticides applied at recommended dose and frequency. 4. As 3) but winter grain exchanged with spring grain. Pesticides applied at recommended dose and frequency.

In the study four different load scenarios were considered (see Table V). The scenarios included both changes in pesticides applied (scenarios 3 and 4), frequency of application (scenarios 2, 3 and 4) and season of application (scenario 3 and 4). The effects on algae and daphnia quantified as probabilities for observing reductions larger than10 % in populations decreased monotonically and almost linear with decreases in FA index from 85 % to 10 % in algae and from 55 % to 10 % in daphnia. Hence, based on these examples the simple FA index as used by the Danish Agency of Environmental Protection does represent a reliable measure of risk estimate. However, it should be noted that the effect of buffer zones required for risk mitigation was not quantified.

68 Annex DD. Toxicity function in Indicators All indicators discussed except FA include a function describing the toxicity relative to the sprayed area (ADSCOR), the Total use (LI) or both (REXTOX). ADSCOR however also includes an element for dosage, but this is entered as a score (0 – 4). By not including numeric or scoring values for toxicity FA implicitly assume that the standard dose varies inversely with the active ingredients’ toxicity towards target organisms, i.e. pesticides dosed at high rates are expected to show a low toxicity and visa versa. The validity of this assumption with respect to non-target organisms was examined by plotting the single species toxicity against the dose rate for those pesticides with the highest influence in the REXTOX indicator (Figure DD). For fish LC50 increased significantly with recommended dose rate of pesticides (all pesticides: r2 = 0.26, p < 0.01; insecticides only: r2 = 0.52, p < 0.01), but for daphnia and algae no relations was found irrespective of pesticide type considered. This indicates that, an inverse relationship between the applied dose of pesticides and the toxic concentrations may not be generally valid for all aquatic organisms. It should however be noted that the examination includes relatively few pesticides and that this may have biased the outcome significantly.

Relation between EC50 for daphnia and dose on field Relation between EC50 for algae and dose on field 1.000 10.000 Herbicide Insecticides Fungicide 0.100 Fungicide 1.000 Herbicide 0.010 0.100 EC50(mg/l)

0.001 EC50 (mg/l) 0.010

0.000 0.001 0.001 0.010 0.100 1.000 10.000 Dose (Kg/ha) 0.1 1.0 10.0 Dose (Kg/ha)

Relation between EC50 for fish og dose on field 10.0000 Figure DD. Scatter plots between dose rate

1.0000 Insecticide and toxicity (expressed as LC50) of Fungicide pesticides to Daphnia, Fish and Algae. 0.1000 Herbicide Pesticides included those with the highest 0.0100 ADR/ToxA ratios in REXTOX indicator EC50 (mg/l) EC50 0.0010 and applied during the period 1992-2000 in Denmark. 0.0001 0.001 0.010 0.100 1.000 10.000 Dose (Kg/ha)

Annex EE. Calculation of FA

Product Sales Crop Dose Area Area FA kg/year kg/ha treated ha in rotation ha

Herbicide 1: 1.000 Winter cereals 75% 0,50 1.500 Spring cereals 25% 0,25 1.000

Herbicide 2: ......

All herbicides: Winter cereals 921.192 804.198 1,15 Spring cereals 723.348 768.704 0,94 ......

All herbicides All crops 2.809.074 2.188.213 1,28

All growth regulators All crops 219.321 2.188.213 0,10

All fungicides All crops 1.085.111 2.188.213 0,50

All insecticides All crops71 407.017 2.188.213 0,19

All pesticides All crops 4.520.522 2.188.213 2,07 Annex FF: Calculation of LI

Sales Organism Toxicity Number of Area Load Index kg/year LC50 etc. toxicity doses in rotation ha

Herbicide 1: 1.000 Fish 1 mg/l 1.000.000

Herbicide 2: ......

All herbicides: Fish 5.903,00 x 106 2.361.233 2.500,0

All growth regulators Fish 0,71 x 106 2.361.223 0,3

All fungicides Fish 2.007,00 x 106 2.361.223 850,0

All insecticides Fish 35.417,00 x 106 2.361.223 15.000,0

All pesticides Fish 43.328,00 x 106 2.361.223 18.350,3

72 73