SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level

Crop Protection xxx (2017) 1e17

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SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level J. Strassemeyer a, *, D. Daehmlow a, A.R. Dominic a, S. Lorenz b, B. Golla a a Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, Stahnsdorfer Damm 81, D14532 Kleinmachnow, Germany b Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, €nigin-Luise-Straße 19, D-14195 Berlin, Germany Ko

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 August 2016 Received in revised form 27 November 2016 Accepted 29 November 2016 Available online xxx

Strategy development for Integrated Pest Management (IPM) requires suitable environmental risk assessment to upscale risks based on environmental parameters, pesticide properties, multi-exposures toxicity assessments and field-specific information on pesticide usage on landscape level. This paper introduces the easy-to-use environmental risk assessment tool SYNOPS-WEB to assess acute and chronic pesticide risks to soil, surface water and pollinators, as well as via leaching to groundwater. SYNOPS-WEB is a free-to-use online tool available for advisors and policy makers within the European Union to compare and assess pesticide risk under realistic application patterns and environmental conditions. The proposed methodology and structure of SYNOPS-WEB are described along with brief usage instructions. Model predictions for aquatic exposure assessment were evaluated on a four-year pesticide monitoring dataset from a small agricultural catchment. SYNOPS-WEB reliably modelled the aquatic exposure of pesticides detected during monitoring and additionally revealed risks from pesticides that could not be detected in surface water due to their high toxicity at concentrations close to the limit of quantification. Further evaluation of the tool is needed to assess model outputs concerning exposure assessment for soil, groundwater and field margin biotopes. Overall, SYNOPS-WEB has the potential to provide relevant information to European farmers, authorities and agronomists for developing and optimizing IPM strategies with particular focus on minimizing environmental risks. The structure of the model allows for future adaption and integration of novel approaches to risk assessment for a wider range of reference organisms and a larger set of mitigation measures. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Pesticide fate exposure Ecotoxicology Plant protection products Spatial analysis Online assessment tools Pesticide application strategies

1. Introduction Global agriculture heavily depends on the use of pesticides to control pests, weeds and crop diseases for food production (Oerke, 2006). Pesticide residues from fields are often found in surface €bel et al., 2014), in groundwater (Hernandez et al., waters (Kna 2008) and in surrounding biotopes (Topping et al., 2015), leading to adverse environmental effects e.g. on aquatic biodiversity, if critical concentrations are exceeded (Münze et al., 2015). Mitigation of such negative environmental effects involves reduction of pesticide use, non-chemical alternatives and promotion of the principles of Integrated Pest Management (IPM) in agriculture

* Corresponding author. E-mail address: [email protected] (J. Strassemeyer).

(Barzmann et al., 2015; Gummert et al., 2013; Mouron et al., 2012; Vasileiadis et al., 2013; Zhang et al., 2015). In Europe, pesticides have to be mandatorily used in a sustainable manner to reduce risks and adverse effects on human and environmental health (EuropeanCommission, 2009; Lamichhane et al., 2016). Hence, there is the need to develop and implement innovative IPM strategies using alternative plant protection methods (Hillocks, 2012). Novel crop production strategies are developed following a design-assessment-adjustment cycle to assure continuous validation and improvement of the IPM solutions (Lescourret, 2014). In order to ascertain the environmental, economic and social sustainability of novel IPM solutions, ex-ante (≙before in-field evaluation) and ex-post (≙after in-field evaluation) assessments are performed during the development process using multi-criteria assessment tools such as DEXiPM (Fortino et al., 2016; Pelzer et al., 2012) or SustainOS (Mouron et al., 2012). These tools

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Please cite this article in press as: Strassemeyer, J., et al., SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level, Crop Protection (2017), http://dx.doi.org/10.1016/j.cropro.2016.11.036

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developed for assessing the sustainability of crop production systems use a decision-tree approach based on semi-quantitative attributes complemented by expert knowledge. However, such approaches do not quantitatively consider the environmental risk of pesticides and therefore need to be complemented by environmental risk assessment approaches. Environmental risk assessment (ERA) evaluates the probability that exposure to one or more contaminants, such as pesticides, or other stressors may result in adverse environmental effects (Damalas and Eleftherohorinos, 2011). Numerous ERA models and indicators have been developed to conduct such assessments (Bockstaller et al., 2009; OECD, 2015; Reus et al., 2002). These indicators vary in their objectives, the temporal and spatial scales, employ different assessment methods such as risk ratios (Kruijne et al., 2011; Reus and Leendertse, 2000), rating systems using hazard or exposure scores (Bergkvist, 2004; Kovach et al., 1992), and fuzzy expert systems (van der Werf and Zimmer, 1998) and assess risk for one or more environmental compartment. Comprehensive risk assessments at field level would ideally use actual pesticide application information along with fine scale data on site and soil conditions, topography, weather and land use. However, many of the existing ERA models and indicators are not suitable for this task, since they lack supporting user interfaces and, hence, require expert knowledge on model usage and on provision of the necessary input data. For example the OECD risk indicator program (Gutsche and Carley, 2002) require some familiarity with Microsoft ACCESS to enter input parameters, start model runs and aggregate calculated risk scores. Additional software is necessary for the graphic presentation of the results. Promising tools such as the risk indicator SYNOPS (Gutsche and Rossberg, 1997; Strassemeyer and Gutsche, 2010) are not applicable at the large European scale but only for specific regions or on local field level. Taking into account the societal demands for sustainable agriculture involving IPM, there is an inevitable need to advance approaches such as SYNOPS for wider application within the European Union (EU). The EU funded project PURE addresses these challenges to provide practical IPM solutions that reduce agricultural pesticide dependency in selected major farming systems in Europe (PURE, 2014). The structural approach of PURE follows a designassessment-adjustment cycle carried out by different working groups. The workflow had to be flexible enough to enable immediate assessment of the newly designed IPM strategies for their risk mitigation potential, and if necessary modify them and reassess their performance. Furthermore, the ERA model required incorporation of toxicity assessments from multiple exposures combined with field-specific information on pesticide usage and land use besides the chemo-physical and toxicological properties of the pesticides. The objective of this paper is to present the development of an easy-to-use environmental risk assessment tool, SYNOPS-WEB, which can be used by non-experts, based on the methodology and algorithms of the existing risk indicator SYNOPS (Gutsche and Rossberg, 1997; Strassemeyer and Gutsche, 2010). The methodology and structure of SYNOPS-WEB, the underlying data and the implemented algorithms are described in detail. Brief instructions on its usage are also provided. The model subroutines for aquatic exposure assessment were evaluated on a pesticide monitoring dataset from a small agricultural catchment. This comprehensive dataset comprises weekly measurements of pesticide concentration in the surface water over a four year period of all pesticides applied on the fields in the catchment during this period.

2. Material and methods 2.1. Model structure of SYNOPS-WEB SYNOPS-WEB is written and coded in Java using Hibernate for database integration, Java Sever Pages and Spring. The online tool is freely available for registered users and can be accessed at http://sf. julius-kuehn.de/synops-web/. SYNOPS-WEB consists of three separate modules (Fig. 1): (i) SYNOPS-WEB Front End: a web based Graphical User Interface (GUI) with an embedded GIS application. The GUI facilitates defining site and application scenarios, calculating risk and interpreting the results; (ii) SYNOPS-WEB model application service: a model application service including the Model Core Library (MCL) which contains the underlying models for environmental risk assessments and (iii) SYNOPS-WEB database: a database to store site information, environmental conditions, physico-chemical properties of pesticides and application scenarios. The SYNOPS-WEB Front End and the model application service are linked by an XML interface that allows for easy communication with external systems and services. 2.2. SYNOPS-WEB model application service: General concept of the acute and chronic ERA The risk indices for ERA were calculated as exposure toxicity ratios (ETR) where the predicted environmental concentration (PEC) is related to a toxicity value of a certain reference species (RS). ETRs were calculated for various RS in the three environmental compartments: soil (RS: earthworm), surface water (RSs: algae, Lemna sp., Daphnia sp., Chironomus sp., and fish) and field margin biotopes (RS: bee). RS toxicity values used in SYNOPS-WEB were derived from Lewis et al. (2016). The loading of the active ingredients (AI) into these compartments were estimated for each pesticide application and a time dependent curve of the PEC was derived. Temperature dependent degradation of the pesticide loads were described by a first order kinetic model (see Appendix A). Over a period of 365 days beginning with the start of the vegetation period, the maximum value of the time dependent PEC curves (PECmax, eq. (1)) and the maximum of the time weighted average concentration of seven days (PECTWA max, eq. (2)) were calculated for each applied AI as measures of acute and chronic exposure, respectively.

PECmax ¼

max PECðtÞ

PECTWA$max ¼

max PECTWA ðt Þ

1t365

Z

where

(1)

1t365

PECTWA ðt Þ ¼

t

t7

PEC ðt Þ

(2)

7

The half maximal lethal/effect concentration (LC50, EC50) and the no effect concentration (NOEC) of each AI were used to describe acute and chronic toxicity, respectively. Acute risk (ETRA(RS;AI), eq. (3)) and chronic risk (ETRC(RS;AI), eq. (4)) for the various RS were calculated from the exposure and toxicity values.

ETRAðRS;AIÞ ¼

ETRCðRS;AIÞ ¼

PECmax L EC50ðRS;AIÞ

PECTWA$max NOECðRS;AIÞ

(3)

(4)

Please cite this article in press as: Strassemeyer, J., et al., SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level, Crop Protection (2017), http://dx.doi.org/10.1016/j.cropro.2016.11.036

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Fig. 1. Modular structure of SYNOPS-WEB: The SYNOPS-WEB Front End and the SYNOPS-WEB model application service are linked by an XML interface (solid arrows). The SYNOPSWEB database stores application scenarios and information on site, environmental conditions and pesticide properties. Input parameters are directly derived from the database via SQL (dashed arrows).

Eqns. (3) and (4) represent the risk from the application of a single AI on one specific field. However, crop protection strategies typically include multiple pesticide applications, and a single pesticide product may contain multiple AIs. The acute risk indicator describes the potential for immediate harmful effect on the RS. Therefore, the acute risk of a full application strategy was considered as the maximum risk posed by all application events and of all AIs applied within one vegetation period, with n representing the number of AIs applied on the same field (eq. (5)).

ETRAðRSÞ ¼ max ETRAðRS;AIi Þ 1in

(5)

In contrast, the chronic risk assesses potential harmful effects that are caused by variable and prolonged exposure of the RS. Therefore, the chronic risk values (ETRC) were aggregated additively for each RS (eq. (6)) according to the concept of concentration addition (CA) (Vaj et al., 2011; Verro et al., 2009; Zhan and Zhang, 2012). AIs with identical modes of action can act mutually to result in synergistic effects that are larger than the effects of each AI applied individually. These effects can be described by CA which has been demonstrated as a suitable method for assessing mixture toxicity (Backhaus and Faust, 2012; Backhaus et al., 2011). The chronic risk aggregation of an application pattern is carried out in two steps. First, the chronic risk values are calculated for each applied AI and added on a daily basis to derive curves of ETR sums. Subsequently, the maximum of these ETR-sum-curves is derived which constitutes the chronic risk of the full application strategy (eq. (6)).

ETRCðRSÞ ¼ max

1t365

X PECTWA ðt; AIi Þ NOECðRS; AIi Þ

(6)

1in

The aquatic risk indices are calculated separately for each RS. The overall acute (eq. (7)) and chronic aquatic risk (eq. (8)) is defined as the maximum of the calculated risk indices of the five aquatic RS fish (fi), algae (al), lemna (le), daphnia (da) and chironomus (ch).

  ETRAðaquÞ ¼ max ETRAðalÞ ; ETRAðdaÞ ; ETRAðfiÞ ; ETRAðleÞ ; ETRAðchÞ (7)   ETRCðaquÞ ¼ max ETRCðalÞ ; ETRCðdaÞ ; ETRCðfiÞ ; ETRCðleÞ ; ETRCðchÞ (8) The terrestrial risk indices have been calculated accordingly (eqs. (9) and (10)). Toxicity data for soil organisms were only available for earthworms (ew). The LC50 and NOEC of the AIs were corrected for absorption by a factor (fc) of 0.5 depending on the octanol-water partition coefficient (log Pow) (EPPO, 2003).

ETRAðsoilÞ ¼ ETRew *fc

if log POW > 2 then fc ¼ 0:5 else fc ¼ 1 (9)

ETRCðsoilÞ ¼ ETRew *fc

if log POW > 2 then fc ¼ 0:5 else fc ¼ 1 (10)

Data for bee toxicity were available as lethal oral dosages (LD50). Since chronic toxicity data for bees was not available, only acute risks were calculated as the maximum of the indices for in-crop exposure and exposure in field margins (eq. (11)).

  ETRAðBeeÞ ¼ max ETRAðbee$cropÞ ; ETRAðbee$FMÞ

(11)

Threshold levels of ETR were applied to characterize the calculated acute risk indices assuming very low risk at ETR < 0.01, low risk at 0.01  ETR < 0.1, medium risk at 0.1  ETR < 1, and high risk at ETR  1. For the chronic risk indices, ETR threshold levels were set at ETR < 0.1 for very low risk, at 0.1  ETR < 1 for low risk, at 1  ETR < 10 for medium risk, and at ETR  10 for high risk. In addition to the terrestrial and aquatic indicators a groundwater indicator based on the Dutch indicator GEO-Pearl (Tiktak et al., 2002) was implemented in SYNOPS-WEB following Kruijne et al. (2011). The calculated values are expressed as threshold exceedance ðTEGW Þ in relation to the threshold for concentration in groundwater of 0.1 mg l1 (European Commission, 1998).

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TEGW ¼

PECgw 0:1

(12)

To characterize the calculated groundwater indices the level of very low probability of exceeding the threshold value is set at TE < 0.1, of low probability at 0.1  TE < 1, of medium probability at 1  TE < 10 and for high probability at TE  10. Detailed information on the exposure assessment methodology is presented in Appendix A. 2.3. SYNOPS-web database 2.3.1. Soil parameters All environmental input parameters necessary for the exposure assessment (Appendix A) were available on 1 km2 grid cells at the European Soil Database (ESDB) v2.0. as described in (Panagos et al., 2012b). The following parameters were derived for each grid cell: the dominant slope class, the organic carbon (OC) content in the top layer (OCtop, 0e0.3 m), the dominant texture class in the top soil and sub soil, the parent material, the depth of the gley horizon, and information about the presence and target and type of water management systems. The soil OC content in the top 1 m of the soil profile (OC1m) was obtained from the average ratio between OCtop and OC1m (OC1m ¼ OCtop*0.538) as described in (Kruijne et al., 2011). The topsoil physical properties for bulk density and field capacity were derived from Ballabio et al. (2016) and the K-Factor of the Modified Universal Soil Loss Equation (MUSLE) was obtained from Panagos et al. (2012a). Information on base flow was derived from the global maps of stream flow characteristics as described in (Beck et al., 2015). Soils are classified by the NRCS (2009) into four hydrologic soil groups, A, B, C and D based on the soil's runoff potential where A describes the lowest runoff potential and D the highest. Hydrologic soil groups were derived from three parameters of the soil geographical database: (Panagos et al., 2012b) texture classes in top and sub soil and the depth of the gley horizon according to Stolbovoy et al. (2007). 2.3.2. Crop parameters The crop of the analysed pesticide application pattern is selected by the user from a list of crops in the menu. These are linked via the EPPO codes to internal crop groups to retrieve the crop development dates, interception parameters (A1.2) and parameters relevant for drift (Table A.1), runoff (Table A.3) and erosion (Table A.4). 2.3.3. Climate data and crop scenarios Monthly temperature averages and monthly precipitation sums were obtained from the WorldClim (http://www.worldclim.org) dataset at a spatial resolution of 1 km2 (Hijmans et al., 2005). Crop scenarios were defined in FOCUS (2000) and have been assigned to European climate zones on the basis of annual average rainfall and annual average temperature. The annual average temperature and precipitation of the WorldClim database were used for classification of these crop scenarios. Kruijne et al. (2011) provides a detailed description of this classification and the corresponding lists of the crop group specific emergence, maturation, harvest and fallow dates. 2.3.4. Surface water parameters All parameters describing surface waters and their connectivity to the fields have to be entered by the SYNOPS-WEB user. According to FOCUS (2001) the following values are given as default and can be modified by the user: surface water width (1 m) and depth (0.3 m), minimum distance from edge of the field to the surface

water (1 m), surface water type (flowing) and vegetated filter strips (no/0 m). 2.3.5. Pesticide properties The properties of AIs, such as sorption coefficient (Koc), logarithm of the octanol-water partition coefficient (logPOW), half-life in soil (DT50soil), half-life in water and/or sediment (DT50water), halflife on plants (DT50foliar), acute (EC50/LC50) and chronic (NOEC) toxicity to aquatic organisms and earthworms, and acute toxicity (LD50) to bees were obtained from the Pesticide Properties Database (PPDB) (Lewis et al., 2016). Pesticide product properties such as the product formulation with additives and the labelled conditions of use were extracted from the online product/label database maintained by BVL (2016). Both databases are updated every 3 months. Additional product databases at national or EU level can be added in future. In case the product is not available in the existing database, AI with the corresponding AI application rate can be entered. 2.4. SYNOPS-WEB front end In order to assess the environmental risk of certain plant protection products under specific environmental and field conditions the user is required to follow a step-wise approach. (i) Define site scenario: The input data for the environmental scenarios are derived from EU-wide GIS maps for soil, climate and elevation. The environmental and soil data are selected by choosing on the map the 1 km2 tile where the site is located. Fixed scenarios are entered manually for the surface water (SW) parameters and the connectivity to surface water. (ii) Define application scenario: The user enters a tractor ride with the application date and the equipment properties (drift reduction), percentage of area sprayed and buffer zones. Subsequently, the applied pesticides are added to the tractor rides either as AI or as products. The user manually enters the application rate of the product or AI. (iii) Combine site and application scenario: It is possible to enter a number of site and application scenarios which can be combined with each other. (iv) Calculate environmental risk: For all selected site and application scenario combinations the environmental risk indicators are calculated according to the underlying models for ERAs in the model application service (see Appendix A). 2.5. SYNOPS-WEB model evaluation Pesticide monitoring data from the small stream Lamme (Continental Bio-geographical Region in Europe, (EEA, 2012) gathered over four years were used to evaluate the SYNOPS-WEB model. The upstream sub-catchment of the Lamme (Fig. 2) is located in Lower Saxony (Germany) and has an area of 194 ha where no settlements are located. The sub-catchment includes eight arable fields of 109 ha which were treated with pesticides during all years of monitoring from 1995 to 1999 (Pestemer et al., 2001). All arable fields in the catchment were cultivated by the same farmer growing winter wheat, winter barley, sugar beets and oilseed rape whose pesticide application events have been monitored. The sample site for surface water samples is located 1.2 km downstream of the spring. Surface water samples (30 ml) were taken hourly by an automatic sampler and integrated to mixed samples of three litres over sample intervals varying from 3 to 12 days (mean 6.12 ± 2.03 days). Samples were analysed using solid-phase extraction with C18-cartridges followed by GC/MS and LC/MS/MS quantification. The limit of quantification for the analysed AIs was 0.04 mg l1. A total number of 143 samples were analysed in the four years which cover 66% of the experimental period (870 from 1320 days). Gaps in monitoring occurred due to weather condition (e.g. low

Please cite this article in press as: Strassemeyer, J., et al., SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level, Crop Protection (2017), http://dx.doi.org/10.1016/j.cropro.2016.11.036

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Fig. 2. Catchment of the small agricultural stream Lamme. Green borders indicate arable fields treated with pesticides. Red borders indicate areas of the catchment not treated with pesticides. The location of the sampling site for surface water samples is marked by the pink dot. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

temperature in winter) or due to technical malfunctioning of the automatic sampler. In total 37 AIs were applied in the Lamme catchment over the monitoring period from which 11 AIs were found in the surface water samples (Table 1), 13 AIs could not be detected and remaining 13 AIs could not be analysed due to methodological constraints. Environmental input parameters for each field were extracted from the maps using SYNOPS-WEB. Local information on slope and necessary surface water parameters were entered manually. Contrary to the default implementation of SYNOPS-WEB we used actual weather data (interpolated grid data and observed weather station

data) from the German Weather Service (DWD, 2016) as it is more region specific than the WorldClim data set. All monitored pesticide application data of the eight fields were entered into the SYNOPSWEB Front End. SYNOPS-WEB calculates the PECsw(t) in the surface water on a daily basis for each field. To compare these results with the measured concentrations of the mixed samples (Cm), the PECsw(t) were summed up over the eight fields on daily basis. From these daily sums the time weighted mean PEC of each sampling interval (PECcat) was calculated. PECcat represents the average PECsw of the catchment for each sampling interval.

Please cite this article in press as: Strassemeyer, J., et al., SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level, Crop Protection (2017), http://dx.doi.org/10.1016/j.cropro.2016.11.036

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Table 1 Pesticide applications and residues in mixed samples from 1995 to 1999. For each active ingredient (AI) the maximum concentration in the surface water, the minimum lethal concentration (LC50) or no effect concentration (NOEC) for the aquatic RS (daphnia fish, algae, lemna and chironomus), the LC50 and NOEC for earthworm (EW) and lethal oral dose (LD50) for honey bee are listed. H ¼ herbicide, I ¼ Insecticide, F ¼ Fungicide, n.d. ¼ not detected. AI

AI applications total volume type treated [kg]

positive samples

positive max samples [%] concentration [mg l1]

minimuma aquatic minimuma aquatic EW LC50 LC50 [mg l1] NOEC [mg l1] [mg kg1]

EW NOEC [mg kg1]

bee LD50 oral [mg g1]

isoproturon chloridazon ethofumesat metamitron carbetamid dimefuron azoxystrobin phenmedipham fluroxypyr epoxiconazol Kresoximmethyl MCPA parathion pirimicarb fenpropimorph prochloraz a-cypermethrin diflufenican esfenvalerat tebuconazol difenoconazol quinmerac propaquizafop fenpropidin

H H H H H H F H H F F

54 8 16 17 2 2 16 16 28 16 19

389.1 73.1 25.2 129.5 24.1 12.0 22.2 25.4 28.9 13.6 17.0

56 37 26 14 12 9 7 5 1 1 1

39.16 25.87 18.18 9.79 8.39 6.29 4.9 3.5 0.7 0.7 0.7

0.0029 0.00108 0.00066 0.00084 0.00094 0.0005 0.00017 0.00106 0.00102 0.00004 0.00004

0.013 3.6 3.9 0.4 81 0.008 0.23 0.086 0.2196 0.0081 0.063

0.005 0.1 0.32 0.04 0.14 0.0018 0.044 0.02 0.0564 0.00081 0.007

1200 1200 134 914 660 300 283 36 1200 1200 562.8

120 400 30 28 60 30 20 6.66 1000 120 937

195 50.4 50 97.2 1725.6 120 25 27.6 100 99.6 14

H I I F F I H I F F H H F

17 5 6 17 3 4 28 11 19 2 8 5 20

58.6 5.9 5.3 36.2 13.0 0.6 13.3 3.0 22.8 1.8 9.1 1.9 34.8

n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

0.152 0.0025 0.017 0.327 0.0066 0.0003 0.00025 0.0001 0.144 0.032 48.5 0.19 0.0057

0.0162 0.00056 0.0009 0.058 0.01 0.00003 0.0001 5.2E-05 0.01 0.0056 3.16 0.019 0.0014

325 320.4 72 600 600 120 600 10.6 1381 732 1200 600 600

32.5 8 7.2 11.28 8.4 22 120 1.06 10 610 120 1.95 10

200 0.21 4 95.6 101 0.059 134.76 0.8 99.66 212.4 130.2 20 10

a

Minimum toxicity values for RS groups: daphnia, fish, algae, lemna and chironomus.

Total measured loads (loadm) and calculated loads (loadc) were estimated by multiplying Cm and PECcat respectively with a stream discharge rate of Qavg ¼ 1.185 m d1 (Beck et al., 2015) for each sample interval and summing them up for the entire 4-year period. Periods with monitoring gaps were not considered in the load assessment. 2.6. Statistical analysis Agreement of PECs from SYNOPS with measured concentrations were tested with Lin's concordance coefficient (Lin, 1989) using the R package epiR (Stevenson, 2016). Lin's concordance coefficient (CCC) measures the agreement between the predicted (PECcat, loadc) and measured (Cm, loadm) variables incorporating precision and accuracy simultaneously. The differences between years of the field specific risk indices were tested using a standard ANOVA test. 3. Results and discussion 3.1. Evaluation of the exposure assessment Eight AIs were tested in each of the 143 samples yielding 1144 measured concentrations (Cm) of AIs. Of these, 765 were not detected in the sample and were not predicted by SYNOPS (Cm ¼ 0 and PECcat ¼ 0), 238 were not detected in the sample but SYNOPS calculated a PECcat (Cm ¼ 0 and PECcat >¼ 0) and 13 were detected in the sample but PECcat was zero (Cm >¼ 0 and PECcat ¼ 0), leaving only 128 values where both PECcat and Cm were greater than zero (Figs. 3A and 4). All AIs considered together, the measured concentrations Cm showed good agreement with the calculated PECcat (Lin's CCC ¼ 0.78, 95% CI ¼ 0.76e0.81) (Fig. 3A). The aggregated calculated load (loadc) and measured load (loadm) of the eight AIs showed even better agreement (Lin's

CCC ¼ 0.85, 95% CI ¼ 0.69e0.93) (Fig. 3B). The AI specific concordance of predicted versus observed concentrations is shown in Fig. 4. It reveals that only three AI's (chloridazon, isoproturon and carbetamid) have moderate to good agreement with CCC > 0.74. Metamitron shows rather poor agreement with a CCC ¼ 0.66. Four AI (azoxystrobin, dimefuron, ethofumesat and phenmedipham) showed very poor or no agreement (CCC < 0.35). One factor possibly contributing to the low agreement of azoxystrobin, dimefuron, and phenmedipham was the low number of samples (n  8) where AIs could be observed. Furthermore, the concentration of most of the few positive samples of these AIs were at a rather low level (Cm < 0.1 mg l1). and close to the limit of quantification. A study conducted in California in which 41 pesticides used during 1992e2009 were analysed with a web based tool showed similar agreement between predicted and observed annual pesticide loads (Zhan and Zhang, 2012). In another model evaluation study of three different catchment models, conducted in the in the Sugar Creek watershed (Parker et al., 2007), PECs of the pesticides atrazine, metolachlor, and trifluralin were compared with field data monitored in flowing water bodies. In this study all three model models showed moderate to low agreement of calculated and measured concentrations. Such results and the analysed agreement in this study demonstrate the difficulty comparing PECs estimated on the basis of spatially and temporally variable soil, weather, and pesticide applications with spatially fixed monitoring data. The moderate agreement of the PECcat and Cm can be related to existing uncertainties in the modelling process and the monitoring data. Firstly, precipitation data comes from an interpolated data set and was not directly measured in the catchment. Precipitation from three nearby weather stations (9e17 km from the catchment) recorded on some days during the monitoring period varied by over 30 mm (DWD, 2016). Such large differences between actual precipitation and precipitation used as model input explain partly the

Please cite this article in press as: Strassemeyer, J., et al., SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level, Crop Protection (2017), http://dx.doi.org/10.1016/j.cropro.2016.11.036

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7

Fig. 3. Relationship between (A) observed (Cm) and predicted concentrations (PECcat) (CCC ¼ 0.78 (95% CI 0.76e0.81)) and (B) between observed (loadm) and predicted loads (loadcat) (CCC ¼ 0.85 (95% CI 0.69e0.93)) of 8 AIs found in the Lamme catchment in 143 samples taken from 1996 to 1999. The dashed line represents the 1 to 1 line.

Fig. 4. Relationship between observed (Cm) and predicted concentrations (PECcat) of single AI in the Lamme catchment. The dashed line represents the 1 to 1 line. The concordance correlation coefficient (CCC) is given with the 95% confidence intervals in parenthesis.

discrepancy between PECcat and Cm. This is also the reason for the 13 values where PECcat ¼ 0 but Cm > 0. Input parameters for soil and slope are derived from GIS maps and might not correspond to the

actual field conditions leading to an over- or underestimation of PECcat compared to Cm. Also the pesticide properties (e.g., KOC, DT50SO, and DT50W) are usually measured under standard

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laboratory conditions and can vary widely under different environmental conditions (Ghafoor, 2013; Weber et al., 2004). Hence, depending on specific environmental conditions, pesticides could differ in their actual and modelled behaviour. The considered environmental settings of the monitoring site and the subset of the measured AIs represent only a small excerpt of the possible model parameterisations and, therefore, are only a first step in the model evaluation process. In general larger monitoring effort in various watersheds and further sensitivity analysis showing the effect of the uncertainties in input parameters on risk indices are required (Zhan and Zhang, 2013). However, the advantage of such a small sub catchment for evaluation is that it excludes additional pesticide influx from outside sources such as non-agricultural land or point pollution from settlements. The presented evaluation approach focuses on the PECs in surface water. Monitoring studies often focus on either water pollution or soil contamination as it is the case with the study presented here. Hence, a comparison of the predicted and measured concentrations in soil, groundwater and on non-target plants was not possible due to absent concentration measurements in these compartments. Therefore ERA for soil organisms, non-target arthropods and groundwater contamination could not be evaluated with the current dataset, but needs to be performed in future studies.

3.2. Risk ratios (ETR) on field level Annually aggregated risk ratios (ETR) in the different environmental compartments for each pesticide application pattern on the eight fields in the sub catchment range from very low to medium risk (Table 2). The acute ð0:009 < ETRAðsoilÞ < 0:0314Þ and the chronic risk ratios in soil ð0:0035 < ETRCðsoilÞ < 0:019Þ and the acute risk to bees ð0:0092 < ETRAðbeeÞ < 0:083Þ range from very low risk to low risk. However, the acute aquatic risk assessment yielded a medium risk in most cases ð0:0135 < ETRAðaquÞ < 0:8456Þ which was in general slightly higher compared to the chronic aquatic risk. Two of the application patterns triggered exceedance of the threshold values for groundwater with TEGW > 1. ANOVA tests showed significant differences (p < 0.001) between the years for ETRCðaquÞ and for ETRAðbeeÞ, indicating that the farmer conducted a change of his crop protection strategy or that exceptional weather events occurred during the four years. On the other hand no significant differences (p > 0.05) between the years were found for ETRAðaquÞ, ETRAðsoilÞ , ETRCðsoilÞ and TEGW indicating that there was no significant change in the risk levels of the applied pesticides for these compartments. The standard field in SYNOPS-WEB is represented by a field area of 1 ha with soil and climate parameters derived from 1 km2 tiles and manually entered parameters describing the surface water scenario. The ETR of one field represents the overall environmental

Table 2 : Calculated risk indices (ETRs) for the eight arable fields in the Lamme catchment. The maxima of the ETR over all reference species for each ETR category are shown. Risk classes: dark green ¼ very low risk, light green ¼ low risk, yellow ¼ medium risk and red ¼ high risk (no high risk index was predicted by SYNOPS-WEB). WW ¼ winter wheat, SB ¼ sugar beet, OR ¼ oilseed rape, WB ¼ winter barley.

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9

Fig. 5. Maximum acute (ETRacute) and chronic (ETRchronic) risk indices of active ingredients applied in winter wheat (WW) and in oil seed rape (OR) during the four monitoring years. The yellow line indicates threshold for medium and the red line for high risk. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

risk posed by the pest management practices on that field which is the smallest management unit for pest control. Different fields usually have different pest management practices, such as choosing different combinations of pesticides or applying pesticides on different dates. Comparing field-level risk ratios of possible application patterns can assist users in optimizing their pest control

strategies and in choosing lower-risk pesticides. Many existing ERA tools only provide risk evaluations at AI-level (FOCUS, 1997; Kruijne et al., 2011). The aggregation for AIs applied on one field has to be performed by the user. In contrast, SYNOPSWEB incorporates risk aggregation of AI applications according to the principle of concentration addition for the chronic risk (ETRC;

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(Backhaus and Faust, 2012)) or as maximum values for the acute risk (ETRA). This lines up with web-based ERA tools which also include risk aggregation on field level such as IpmPRIME (Guzy et al., 2014) and DSS-PURE (Zhan and Zhang, 2012) or standalone tools like EPRIP2 (Trevisan et al., 2009). Multicriteria assessment tools like DEXiPM (Fortino et al., 2016; Pelzer et al., 2012) or SustainOS (Mouron et al., 2012) evaluate sustainability of crop production strategies taking into account a large number of attributes. Important attributes for these tools are the environmental performance of the strategies. SYNOPS-WEB can provide this input in a simple way as the aggregated risk of the strategy for aquatic and terrestrial organisms and as risk to groundwater. 3.3. Risk ratios (ETR) on AI level The maximum risk indices with different AIs applied are exemplarily shown for winter wheat and oil seed rape (Fig. 5). The highest risk to aquatic organisms comes from isoproturon, used for winter wheat. It was also the most frequently applied (389 kg in 54 applications, Table 1) and the most frequently detected AI in the sub-catchment. SYNOPS-WEB predictions show that a reduction of isoproturon could have improved the water quality and risk situation in the surface water substantially (Fig. 5). A total of 13.3 kg of diflufenican was applied in 28 applications. SYNOPS estimated medium risk scores for diflufenican (Fig. 5) though it was never detected in the water samples. This is due to the very low LC50 (0.25 mg l1) and NOEC (0.1 mg l1) (Lewis et al., 2016) values of diflufenican triggering medium acute risk already at concentrations below 0.025 mg l1, which is below the limit of quantification for all AIs in the SW monitoring tests (0.04 mg l1). A reduction in the application rate of diflufenican would have likely reduced the predicted aquatic risk though it could not be detected in the water samples. This is a noteworthy argument to why it is necessary to complement monitoring studies with suitable ERA tools. Another trigger for high aquatic risk comes from dimefuron (Fig. 5). It was used on oilseed rape that is only grown on one of the fields in the catchment and only in three of the four years of the monitoring period. The trigger is surprising given that only 12 kg were applied over two applications and was detected in eight of the samples. A reduction or omission of the two dimefuron applications could have considerably improved the water quality and risk situation in the surface water. SYNOPS-WEB users can view the predicted ETR for the applied AIs at field level. This enables comparison of application strategies, potential substitution of high-risk pesticides by low-risk pesticides, or reduction of application rates of high-risk products. The analysed applications patterns were based on a monitoring conducted between 1995 and 1999. Some of the applications are already out-of-date, for example, the dimefuron applications are replaced in today's pesticide use calendars for oil seed rape pesticides with dimefuron have only been authorized in Germany until 2004 (BVL, 2016). Annual surveys of pesticide use in 120 German reference farms (Freier et al., 2014) demonstrate that isoproturon is only used in a small portion (9e18%) of the surveyed annual application calendars (n ¼ 179e261). Diflufenican is used more frequently with 36e48% of farmers applying this AI. A replacement of isoproturon or diflufenican would be feasible only when it is not opposing the resistance strategy and farmers can maintain sufficient production. 4. Conclusion Environmental risk assessment (ERA) tools such as SYNOPS-

WEB are an essential part of modern agriculture. Testing and implementation of novel IPM strategies could be done faster and is financially more efficient. While being an excellent complement to environmental monitoring studies, ERA tools also have the potential to highlight issues that are not noticeable from monitoring studies as in the case of diflufenican. SYNOPS-WEB allows farmers and advisors in Europe to conduct a realistic risk assessment of pesticide applications under site-specific conditions. It also provides for choosing from different alternatives available towards risk mitigation. The initiation of comparative assessment at user level is one of the subjects of EU 2009/128/EG (European Commission, 2009) and subsequently is an element in the national action plans of the EU member states (Barzman and Dachbrodt-Saaydeh, 2011). Pesticide risk is not a the primary determinant for farmers' decisions on pesticide applications (Andert et al., 2015). We believe that online tools such as SYNOPS-WEB with user-friendly interfaces are inevitable instruments for farmers, advisors and authorities to encourage taking environmental risk from pesticide use into consideration while planning IPM strategies. However, such online tools need to be out-balanced by other selection criteria such as efficacy on target pests, costs of the product, resistance, residues, and ideally integrated in a collective platform. Benchmarking systems that allow comparison of a farmer's performance with that of fellow farmers' are additional incentives to using similar ERA tools (Kragten and De Snoo, 2003). There is a lot of scope for further improvements in SYNOPSWEB. The integration of more mitigation measures such as strip till techniques, mulch seeding, the creation of buffer strips (Reichenberger et al., 2007) or multifunctional field margins (Haddaway et al., 2016; Stutter et al., 2012) offer several benefits addressing the sources of risk in vulnerable fields (Alix et al., 2015). Such measures would provide a larger set of options for farmers to efficiently lower pesticide risks to the environment. Furthermore, could be extended to cover human health and a wider range of economically and environmentally important organisms such as beneficial arthropods, soil organisms or non-target plants. This would allow for a more holistic risk assessment in the process of selecting a suitable IPM. Acknowledgments The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/20072013) under the grant agreement no 265865. We thank Peter Horney for his assistance in data processing and two anonymous reviewers for their helpful comments on an earlier stage of the manuscript. Appendix A A.1. Details of the exposure assessment Many complex models were developed to calculated pesticide concentrations in soil and water bodies such as MACRO (Jarvis, 1994), PRZM (Carsel et al., 1995), or SWAT (Nietsch et al., 2005). However, the aim of SYNOPS-WEB is not to simulate dynamic concentration but to compare risks under specific environmental conditions. Thus, simplified deterministic and empirical models are adopted for calculating PECin time and space. A.1.1. Degradation in soil, water and on plants The daily and temperature dependent concentration PECj ðtÞ is calculated for each application, drift or runoff event of an active ingredient (AI) during the time period after such an exposure event

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(t > tev) assuming a first order degradation (eq. (A1-2)). The index j represents the three compartments, soil (soil), surface water (SW) and plants. On the day after the initial event (t ¼ tev þ 1) the concentration PECj ðtÞ is calculated according to eq. A.1, where kj(t) [day1] is the disappearance rate in the three compartments (eq. A.3) and fj(t) is the corresponding temperature correction factor (eq. A.4). For all successive days (t > tev þ 1) the temperature correction is based on the PEC of the previous day (t  1) as given in eq. A.2.

PECj ðtÞ ¼ iPECj ðtev Þ$e

kj ðtev þ1Þ

PECj ðt Þ ¼ PECj ðt  1Þ,ekj ðt Þ kj ðtÞ ¼

if t ¼ tev þ 1 if t > tev þ 1

ln2 fj ðtÞ,DT50j:ref

(A.1) (A.2)

(A.3)

! eEtrans , R

fj ðtÞ ¼ e

1  1 Tref Tj ðtÞ

(A.4)

DT50j.ref [days] is the nominal half-life in water, soil or on non-target plants at reference temperature (Tref, [K]). Etrans is the molar enthalpy of transformation (54000 J mol-1), R is the molar gas constant (8.314 J mol1 K1)), and Tj (t) [K] is the site specific temperature in the compartment at day t. Since on the European level climate data was only available on monthly and not on daily time steps the monthly air temperatures were used for soil Tsoil(t) and plants Tntp(t) temperatures. The surface water temperature (Tsw(t)) was estimated according to Stefan and Preud'homme (1993) based on the air temperature Tair(t) [ C] as in eq. A.5.

Tsw ðtÞ ¼ 5 þ 0:75Tair ðtÞ

(A.5)

A.1.2. Crop interception The percentage of the AI intercepted by the crop (I(t), [%]) is determined based on the crop characteristics, the location and the date of application event (t). For each crop the range of the crop interception is given by a minimum (Imin) and a maximum interception (Imax, Table A.1). Crop growth is characterized by four typical dates: day of emergence (te), day of maturation (tm), day of harvest or first day of senescence (th), and day of fallow land (tf ¼ th þ 30). Before the day of emergence the crop, interception is Imin (eq. A.6), from emergence until maturation interception increases according to eq. A.7. Upon maturation crop interception is assumed to remain constant and a maximum (Imax, eq. A.8) until the start of senescence after which interception is gradually reduced to a minimum (Imin, eq. A.9)

IðtÞ ¼ Imin

tf < ¼ t < te

I ðt Þ ¼ Imin þ ðImax  Imin Þ$fe ðt Þ IðtÞ ¼ Imax

(A.6) te < ¼ t < tm

tm < ¼ t < th

I ðt Þ ¼ Imax  ðImax  Imin Þ,fs ðt Þ

(A.7) (A.8)

th < ¼ t < tf

(A.9)

The time-dependent factors fe(t) and fs(t) are given by eq. A.1011 including the empirical parameters c1 (-0.07), c3 (100), c2 which is dependent on the length of the crop emergence stage and c4 which is dependent on the length of the senescence stage:

11

fe ¼ ð1 þ c1 ,ec2 ,ðtte Þ Þc3

(A.10)

where c2 ¼ 8:2246,ðtm  te Þ0:9855 fe ¼ ð1 þ c1 ,ec4 ,ðtth Þ Þc3 0:9855  where c4 ¼ 8:2246, tf  th

(A.11)

The crop specific default values for Imax, Imin, te ,tm and th where taken form the Focus surface water scenarios (FOCUS, 2001) and the FOCUS groundwater scenarios (FOCUS, 2000). In FOCUS (2000), a groundwater scenario location was assigned to European climate zones on the basis of the annual average rainfall and annual average temperature. These FOCUS groundwater scenario locations define the regions of similar growth stages and were used to determine the crop scenarios in relation to the position of the selected site as a default value, which can be modified by the user within the tool. Table A.1 Minimum interception factor Imin and maximum interception factor Imax as defined in (FOCUS, 2000) and (FOCUS, 2001) Crop group in SYNOPS-WEB

Imin

Imax

Apples Beans (field) Beans (vegetables) Bush berries Cabbage Carrots Citrus Cotton Grass Hops Legumes Linseed Maize Oil seed rape (summer) Oil seed rape (winter) Olives Onions Peas (animals) Pome/stone fruit Potatoes Soybean Spring cereals Strawberries Sugar beets Sunflower Tobacco Tomatoes Vegetables, bulb Vegetables, fruiting Vegetables, leafy Vegetables, root Vines Winter cereals

0.5 0 0 0.5 0 0 0.7 0 0.4 0 0 0 0 0 0 0.7 0 0 0.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0.8 0.7 0.8 0.8 0.9 0.8 0.7 0.75 0.75 0.7 0.7 0.9 0.75 0.75 0.75 0.7 0.6 0.85 0.7 0.7 0.75 0.7 0.6 0.75 0.75 0.75 0.8 0.4 0.7 0.7 0.7 0.7 0.7

A.1.3. Predicted Environmental Concentrations (PEC) in soil It is assumed that the proportion of the application rate that remains after correction for crop interception is entirely deposited at the soil. Therefore, the pesticide load into the soil at the day of an application event (tA) is AR(1I(tA)) where AR [g ha1] is the application rate and I(tA) is the fraction of AR intercepted by the crop at tA. The initial PEC in the soil (iPECsoil(tA)) [mg kg1] is then calculated by considering the soil bulk density (r, [kg m3]) and assuming a mixing depth of d ¼ 0.05 [m] (eq. A.12).

iPECsoil ðtev Þ ¼

AR,ð1  IðtA ÞÞ $0:1 d,r

(A.12)

The time dependent soil concentrations on the days after

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application PECsoil ðtÞ are calculated according to eq. A.1-2. In case of repeated applications of the same AI (nA > 1), the total concentration in the soil is calculated by summing the concentrations of each application on daily basis (eq. A.13).

PECsoil ðtÞ ¼

x X

PECsoil ðt; nA Þ

nozzle of the spray equipment (x, [m]). Various regression functions have been derived from these drift tables (FOCUS, 2001; OECD, 2000). We adopted the functions from FOCUS (2001) where the drift deposition is integrated over the width of the surface water body (Wsw, [m]) by the sum of two sequential power functions connected at the hinge distance (H, [m], eq. A.15). A simplification is possible if the minimum distance from edge of the field to the closet edge of the surface water (minDsw, [m]) is larger than the hinge distance (H  minDsw, eq. A. 15a) or when the entire water surface is located before the hinge point (H  (minDsw þ Wsw, eq. A. 15b)

(A.13)

nA ¼1

0

A.1.4. PEC in surface water The concentration of pesticides in the surface water is influenced by events of drift, runoff, erosion and drainage. Thus, the pesticide concentration in surface water (PECsw(t)) [mg l1] may change on the day of application through exposure by drift (PECsw.d(t)) and on precipitation days by AI dissolved in the runoff (PECsw.r(t)) and drainage water (PECsw.dra(t)) and adsorbed to erosion particles (PECsw.e(t)). The concentrations due to these exposure pathways are calculated separately and added on daily basis (eq. A.14). Depending on the surface water type the PECs are multiplied by a factor (fx(1)) accounting for the remaining AI left in the surface water on days after the exposure events. For standing surface waters it is fx(t) ¼ 1 assuming there is no outflow of water, for flowing surface water fx(tev) ¼ 1 on the day of the exposure event and fx(t) ¼ 0 on days after the exposure event assuming that the AI in the water body is completely removed within one day after exposure.

B fd ¼ @A

ZH



 x dx þ C

minDZsw þWsw

B



D

x



1 C dxA$

H

minDsw

fm Wsw 100 (A.15)

0 B fd ¼ @A

minDZsw þWsw





1

C xB dx A$

fm Wsw 100

if H  ðminDsw þ Wsw Þ

minDsw

(A.15a) 0 B fd ¼ @C

minDZsw þWsw



1  fm C xD dx A$ if H < ¼ minDsw Wsw 100

minDsw

(A.15b)

PECsw ðtÞ ¼ PECsw:d ðtÞ$fx ðtÞ þ PECsw:r ðtÞ$fx ðtÞ þ PECsw:e ðtÞ$fx ðtÞ þ PECsw:dra ðtÞ$fx ðtÞ

fd is the fraction of the applied AI transported to the surface water, fm is a reduction factor which considers improved spraying equipment (50% reduction fm ¼ 0.5, 90% reduction fm ¼ 0.1) and A, B, C and D are empirical regression parameters (Table A.2).

(A.14) The PECs in the surface water from drift, runoff or erosion are

Table A.2 Parameters (A, B, C and D) and hinge distance (H) for 90th-percentile drift functions (FOCUS 2001). Crop groups

Growth stage

arable crops þ small vegetables<0.5m large vegetables>0.5m Hops Vines

all stages all stages all stages not in mature sage mature sage not in mature sage mature sage

Fruits aerial app.

A

B

C

D

H [m]

2.7593 44.769 58.247 15.793 44.769 66.702 60.396 50.470

0.9778 1.5643 1.0042 1.6080 1.5643 0.7520 1.2249 0.3819

2.7593 44.769 8654.9 15.793 44.769 3867.9 210.70 281.1

0.9778 1.5643 2.8354 1.6080 1.5643 2.4183 1.7599 0.9989

1* 1* 15.3 1* 1* 11.4 10.3 16.2

*if the drift coefficient does not dependent on to H then C ¼ A and D ¼ B and H ¼ 1. This was set to generalize the calculations by using the same set of equations for all crop groups.

calculated assuming a standard square field with an area (A) pffiffiffiffi unloading into a surface water body of length A having a trapezoid shape with the sides angled at 45 . Based on the depth (Dsw, [m]) and width (Wsw, [m]) of the surface water the cross section area (Csw, [m2]) of the surface water is Csw ¼ Wsw Dsw  D2sw . For precise exposure assessment daily precipitation sums are needed. This high temporal resolution of precipitation data was not available for total European area. Therefore, monthly precipitation sums were used as a worst case scenario assuming that a rain event of one third of the monthly precipitation sum occurs 3 days after application.

A.1.4.1. Surface water PEC caused by drift. The spray drift is calculated on the basis of drift tables published by the BBA (Rautmann et al., 2001). These tables describe the percentages of applied pesticide drifting beyond the borders of a treated field depending on the crop type, the crop stage and the distance from the last

The initial PEC caused by drift (iPECd(tA)) [mg l1] at the day of application (tA) is calculated according to eq. A.16 where the factor 0.0001 is a unit conversion factor.

Wsw iPECd ðtA Þ ¼ AR$fd $ $0:0001 Csw

(A.16)

The time dependent concentrations in the surface water on the days after the drift event ðPECsw:d ðtÞÞ are calculated according to eq. A.1-2 considering the degradation of the AI. A.1.4.2. Surface water PEC caused by runoff. The initial PEC from runoff (iPECr(tP)) [mg l1] on the day of precipitation (tP) is calculated as described in eq. A.17.

Q Wsw iPECr ðtP Þ ¼ AR$ $fw ðtP Þ$fsl $ $0:0001 Pev Csw

(A.17)

and time dependent concentrations in the surface water on the

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days after the runoff event ðPECsw:r ðtÞÞ are calculated according to eq. A.1-2. In eq. A.17 Pev [mm] is the volume of the precipitation event and Q [mm] is the quantity of water lost by runoff calculated using a modified Curve Number method developed by Lutz (1984) which has been applied for runoff assessment in Germany by Bach € pke et al. (2004). The fraction of the application et al. (2000) and Ro rate dissolved in the water phase at the day of precipitation (fw(tP)) is assumed to be available for run-off and depends on the plant interception at the day of application (tA), the disappearance rate of the AI in soil during the period from application to the precipitation event (tP  tA, A.18)

fw ðtP Þ ¼ eksoil ðtP tA Þ ð1  IðtA ÞÞfw0

(A.18)

fw0 is the initial fraction of AI dissolved in the soil water phase, which is calculated according to eq. A.19-20.

fw;0 ¼

1 1 þ Kd

(A.19)

fsl ¼ 0:021353 slope þ 0:001423 slope2 if slope < 20% else fsl ¼ 1

(A.21)

The surface run-off in this model is calculated according to the modified CN-method of (Lutz, 1984), based on methods of the US Soil Conservation Service (US-SCS, 1990) for four different hydrological soil groups and different land cover classes (Table A.3).

Q ¼ ðP  Ia Þ,CN þ

CN 

a

 eðaðPIa ÞÞ  1 if P  Ia > 0 else Q ¼ 0 (A.22)

The initial losses (Ia) [mm] account for the processes of interception, initial infiltration and surface storage and are estimated according to (Rode, 1995) as in eq. A.23. Table A.3 Curve numbers (CN) dependent on hydrological soil groups and land cover classes (Anderl, 1975) Land use

Hydrological soil groups

fallow arable crops row fruits other feeding crops fruits grassland

1 1 Ia ¼ 7:62 CN

k2

k3

a ¼ k1 $e wn $e Qb

(A.24)

Lutz (1984), Rode (1995) and Maniak (1992) suggest the following values for the calibration factors: kB1B ¼ 0.05, kB2B ¼ 4.6 for arable land or kB2B ¼ 2.0 for forested land and kB3B ¼ 2.0. The week number correlates to the chronological number of the calendar week (week, eq. A.25).

wn ¼ jweek  26j

(A.25)

The information on base flow was derived from the global maps of stream flow characteristics as described in Beck et al. (2015). The 99th percentile computed from the daily flow data (Q99) was used to represent the base flow. The factor 86.4 is a unit conversion factor to convert [mm d1] in [l s1 km2] (eq. A.26).

(A.26)

(A.20)

Koc is the sorption coefficient of the compound [dm3 kg1] and OC the fraction of organic carbon in the top layer of the soil [kg kg1]. The Factor fsl in eq. A.17 reflects the influence of the slope according to (Beinat and van den Berg R., 1996).



A.24). Alpha sets the current soil saturation, expressed by the base flow (QBbB) [l s1km2] in relation to a seasonal variation which is described by the week number (wn).

Qb ¼ 86:4Q 99

Kd ¼ OCKoc

13

A

B

C

D

0,71 0,54 0,55 0,46 0,17 0,20

0,83 0,70 0,68 0,49 0,46 0,46

0,89 0,80 0,81 0,75 0,63 0,63

0,93 0,85 0,86 0,81 0,72 0,72

A.1.4.3. Surface water PEC caused by erosion. A common approach is to consider only the contribution of AI dissolved in water and neglect the part bound to the eroded soil particles since for most active substances and erosion situations contribution to the ECsw is to a great extent from dissolved pesticides (Agassi et al., 1995; Burgoa and Wauchope, 1995). Although this is true for most parts of Europe it might be still necessary to estimate the risk in surface waters caused by sorptive AI bound to eroding soil particles especially in regions with steep slopes. The model used here is based on the Modified Universal Soil Loss Equation (MUSLE) (Wischmeier, 1984), and is analogous to the approach in FOCUS (2001). The total amount of pesticide reaching the surface water by erosion is determined by the erosion load function, which is based on the fraction of application rate bound to the solid phase (fs) and on the ratio of the soil erosion volume (Xe) [t] to the soil volume in the ploughing layer ðA,d,rÞ [kg]. The initial PEC of pesticides transported by erosion particles (iPECs(tP)) [mg l1] on the day of precipitation (tP) is calculated as described in eq. A.27.

iPECe ðtP Þ ¼ AR$

(A.23)

The original CN-method considers the fact, that run-off relates to the soil moisture at the time run-off event occurs. Therefore CN are given for various soil moisture classes, which are derived from the precipitation sums 5 days before the run-off event (McCuen, 1981). Lutz (1984) considers the influence of soil moisture and the seasonal variation using the proportionality coefficient a (eq.

(A.27)

and the time dependent concentrations in the surface water on the days after the erosion event ðPECsw:e ðtÞÞ are calculated according to eq. A.1-2. Analogous to eq. A.21 fs(t) is estimated according to eq. A.28

fs ðtÞ ¼ eksoil ðttA Þ $ð1  IðtA ÞÞ$fs0

(A.28)

where fs0 is the initial fraction of AI bound to the soil phase, which is calculated according to eq. A.29.

fs0 ¼



Xe Wsw $fs ðtÞ$ $0:0001 A$d$r Csw

Kd 1 þ Kd

(A.29)

The erosion model calculates the volume of erosion of the considered field as in eq. A.30

0:65  Xe ¼ 0:79$ Q  qp $A0:009 $usleK$usleLS$usleC$usleP (A.30)

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J. Strassemeyer et al. / Crop Protection xxx (2017) 1e17

Where the soil erosion volume is calculated for a treated unit area (A ¼ 1 ha). For the peak storm run-off value (qp) [mm] an equal distribution over 6 hours was assumed for the precipitation event that triggers the erosion event:

qp ¼

Q 6

(A.31)

The support praxis factor usleP is set to a constant default value of 0.5 (FOCUS, 2001). The vegetation and management factor usleC is linked to the crop as shown in Table A.4 and the erodibility factor usleK is derived on European level according to (Panagos et al., 2012b). The topographic factor usleLS is calculated in eq. A.32.

  A sx2  usleLS ¼ 0:065 þ 0:0456 $slope þ 0:006541$slope2 $ 22:1 (A.32) where sx ¼ 0.2 for slope  1, sx ¼ 0.3 for 1 < slope  3, sx ¼ 0.4 for 3 < slope  5 and sx ¼ 0.5 for 5 < slope. Table A.4 Management factor usleC for different crops at three growth stages. Crop Arable crops Grass Vine Hops Pome-/Stone-Fruits Olives, Citrus, Cotton

Fallow stage

Emergence and mature stage

Senescence stage

0,9 0,02 0,2 0,9 0,9 0,2

0,2 0,02 0,2 0,2 0,2 0,2

0,4 0,02 0,2 0,4 0,4 0,2

Scenarios (FOCUS, 2001). The initial PEC of pesticides from runoff (iPECr(tP)) [mg l1] at the day of the drainage event (tP) is calculated as described in eq. A.33.

iPECdra ðtP Þ ¼ AR $fdr ðfw ðtP Þ; soilÞ$0:0001

(A.33)

where fdr ðfw ðtP Þ; soilÞ is the fraction of AR estimated according to the meta model which is dependents on the soil type and the portion of AR available for drain flow fw ðtP Þ at the day of the precipitation event (see eq. A.18). The time dependent concentrations in the surface water after the drainage event ðPECsw:dra ðtÞÞ are calculated according to eq. A.1-2. The percentage of pesticide lost in the drain flow can be calculated from a relationship between percentage of loss and fw ðtdr Þ. This relationship is dependent on the soil (primarily the texture which influences the transport of pesticides via preferential flow. The meta model was established for soil types representative of the EU which were selected based on the work of the FOCUS Surface Water Scenarios working group (FOCUS, 2001). For each soil type an empirical relationship between fs and fw was derived based on a number (n > 80) of MACRO runs. These relationships for the five FOCUS soil types are described by a 5th order polynomial function (eq. A.34).

fdr ðfw ; soil1 Þ ¼ a$fw 5 þ b$fw 4 þ c$fw 3 þ d$fw 2 þ e$fw

(A.34)

where a, b, c, d and e are empirical parameters listed in Table A.5

Table A.5 Parameters of the relationship of drainage loss to availability in soil. r2

Soil type

Parameters of drainage relationship a

b

c

d

D1 D2 Silty loam D4 D5

6.675  109 1.736  108 2.736  109 7.645  1010 5.274  1010

1.2631  106 5.3990  106 3.654  107 1.485  107 1.791  107

3.906  105 5.864  104 3.079  105 7.313  106 2.177  105

2.928 2.658 1.741 7.709 1.070

A meta model based on MACRO (Jarvis, 1994) was applied to estimate the drainage input into the surface water body. MACRO is used as a drainage model to simulate the FOCUS Surface Water

e     

103 102 103 104 103

2.948 1.174 9.940 7.187 9.692

    

102 101 103 103 103

0.993 0.999 0.993 0.984 0.996

The Soil Geographic Database of Europe was used to identify the geographical location of the drained land and to link them to their attributes (Table A.6).

Table A.6 Linkage between drained soils and the corresponding STU attributes in the Soil Geographic Database of Europe (Panagos et al., 2012b). Soil type General soil properties

Corresponding STU attributes Depth to groundwater

D1 D2

Clay soil with groundwater at shallow depth 5 m Clay soil over a soft impermeable clay >5 m or absent substrate Heavy silt loam soil All

Silty loam D4 Medium loam with a slowly permeable >5 m or absent substrate D5 Heavy loam soil with groundwater at shallow 5 m depth

Texture class

Parent material Water management presents (WM1)

Water management type (WM2)

4 4

3,5,6,7 3,5,6,7

3,4,5,6 4,5,6

3

All 310, 312, 313, 314 All

3,5,6,7

4,5,6

2

All

3,5,6,7

4,5,6

2

All

3,5,6,7

3,4,5,6

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J. Strassemeyer et al. / Crop Protection xxx (2017) 1e17

For drainage simulation it is important to know if precipitation occurs within the field capacity period of the soil. Drainage will only occur during a predictable period, when the soil is at or close to field capacity. Within the field capacity period drainage occurs dependent on precipitation events. Outside the field capacity period no drainage will occur. The start of field capacity period is the first day of the month immediately following the month in which rainfall first exceeds evapotranspiration. The end of field capacity is the first day of the month in which evapotranspiration first exceeds rainfall. A.1.5. PEC in ground water The indicator for the risk of leaching towards deep groundwater layers is based on the long-term average leaching concentration in the soil solution at 1 m depth. Exposure is calculated based on the nominal leaching concentration (CL) [mg l1], the fraction of AI depositing on soil (1  I(tA)) and the application rate (AR). The leaching of AI into groundwater is calculated on the basis of the model GeoPEARL (Tiktak et al., 2002) as described in (Kruijne et al., 2011). The exposure concentration is the long-term average concentration in the soil solution leaching at 1 m depth towards deeper groundwater layers. In case of repeated applications of the same AI (nA > 1) the PECGW is calculated by summing the concentrations of each application (eq. A.35).

PECGW ¼

X

   ARi $ 1  I tappi $CLi

15

exposure this is only true for crops which attract bees during the flowering period. In this case the probability of exposure is set to Pex.crop ¼ 1 if the application date is after the emergence date and before the senescence date of the crop.

Table A.7 Exposure probability of bees in field margins. Start doy

End doy

End date

Pex.FM

1 32 60 75 91 106 121 136 152 167 182 197 213 228 244 259 274 289 305 335

31 59 74 90 105 120 135 151 166 181 196 212 227 243 258 273 288 304 334 366

31-Jan 28-Feb 15-Mrz 31-Mrz 15-Apr 30-Apr 15-Mai 31-Mai 15-Jun 30-Jun 15-Jul 31-Jul 15-Aug 31-Aug 15-Sep 30-Sep 15-Okt 31-Okt 15-Nov 30-Nov

0 0 0.3 0.4 0.6 0.7 0.9 1 1 1 1 0.9 0.8 0.5 0.4 0.2 0.1 0 0 0

(A.35)

1In

References A.1.6. PEC on non-target plants and crops The initial PEC in the field margin caused by drift (iPECFM(tA)) [g ha1] and in the crop (iPECcrop(tA)) at the day of application (tA) are calculated according to eq. A.36-A.37. The fraction of drift into field margin biotopes (fd) in eq. 36 is estimated as described in eq. 15 by integrating the drift function over the width of the field margin (WFM). It is assumed that the minimum distance between the edge of field and the edge of field margin (minDFM) is always 0.5 m.

iPECFM ðta Þ ¼ AR$fd

(A.36)

iPECcrop ðta Þ ¼ AR$Iðta Þ

(A.37)

The temperature dependent concentrations on the days after application PECFM ðtÞ or PECcrop ðtÞ are calculated according to eq. A.1-2. In case of repeated applications of the same AI (nA > 1) the total concentration on the plants is calculated by summing the concentrations of each application on daily basis (eq. A.38-39). x X

PECFM ðtÞ ¼

  PECFM t; napp $Pex:FM ðtÞ

(A.38)

napp ¼1

PECcrop ðtÞ ¼

x X

  PECcrop t; napp $Pex$crop ðtÞ

napp ¼1

(A.39)

if te < tA < th then Pex$crop ¼ 1 else Pex$crop ¼ 0

A.1.6. Exposure probability of bees The probability of bees being exposed by an AI depends on the probability of bees being present or not. Therefore the PEC relevant for bees in the field margin is modified by an exposure factor (Pex.FM(t)) ependent on the calendar date (Table A.7). For in-field

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