Annex 4 analytical methods

model description

general description

acronym: SYNOPS-GIS

name: Model for synoptic assessment of risk potential of chemical plant protection products

main purpose: SYNOPS-GIS evaluates the environmental risk on regional level for terrestrial and aquatic not target organisms by calculating the risk indices on field level and aggregating these for regional extends.

homepage: https://synops.julius-kuehn.de/

Developer and its nature

ownership: Third-party ownership (commercial companies, Member States, other organisations, …)

ownership additional info: Julius Kühn Institute, Federal Research Centre for Cultivated Plants,Erwin-Baur-Str. 27,06484 Quedlinburg, Germany

is the model code open-source?: NO

Model structure and approach with any key assumptions, limitations and simplifications

details on model structure and approach

The risk indicator SYNOPS models pesticide fluxes via different pathways and the resulting concentrations in soil, surface waters and field margins and therefore provides a quantitative assessment of the environmental risk due to pesticides {Strassemeyer 2017}. Risks associated with plant protection products are assessed on field level by linking geospatial data of agricultural fields in in a considered region (InVeKOS), surface waters {AdV 2015}, topography {AdV 2008}, soil characteristics {BüK 2007}, and weather data {DWD 2016}, to a of pesticide use on the specific fields. Field-specific input data for SYNOPS includes the relevant biophysical soil parameters (e.g. organic carbon content, hydrological soil class, soil texture, and field capacity), average slope, daily weather (precipitation and temperature), field margin width, and connectivity to surface waters for all the all considered agricultural fields. Information on plant protection products (active substances, concentrations, labelled mitigation measures) are derived from the German product database, and physico-chemical properties of the active substances were obtained from the Pesticides Properties Database {Lewis2016}.

A short summary of the method described in Strassemeyer et al. {Strassemeyer2017} is presented here. Risk indices are expressed as the Exposure Toxicity Ratio (ETR), calculated as the ratio of the Predicted Environmental Concentration (PEC) to the toxicity endpoints half maximum effect concentration, lethal concentration, lethal rate, lethal dose, and no-effect concentration for specific reference species. The following reference species are considered: algae, aquatic invertebrates, fish, higher aquatic plants, and sediment organisms for aquatic environments; earthworm and springtails for soil; and honeybees, Aphidius rhopalosiphi, and Typhlodromus pyri for field edge habitats. Daily loads of the active substances to the three environmental compartments and a time-dependent curve of PEC were derived. Over a 365-day period, beginning with the first day of the growing season, the 90th percentile of the time-dependent PEC curves and the 90th percentile of the seven-day time-weighted average concentration are calculated to represent the worst-case scenario of acute and chronic exposure for each active substance. The acute toxicity endpoints multiplied with a factor of 0.1 and the no-effect concentration of each active substance were used to describe acute and chronic toxicity, respectively. In order to assess the mixture toxicity of the complete crop protection strategies with multiple fungicide applications and multiple active substances, the acute and chronic risk of the active substances were aggregated according to the principle of concentration addition {Zhan2012, Verro2009}. The risk values were added on a daily basis to derive ETR sum curves and the temporal 90th percentiles to represent the overall acute or chronic risk of a complete application calendars. The risk for the each compartment is calculated as maximum risk of the considered reference organisms.

Strassemeyer J, Daehmlow D, Dominic AR, Lorenz S and Golla B, SYNOPS-WEB, an online tool for environmental risk assessment to evaluate pesticide strategies on field level. Crop Prot 97:28–44 (2017).

Strassemeyer J and Golla B, Berechnung des Umweltrisikos der Pflanzenschutzmittelanwendungen in den Vergleichsbetrieben mittels SYNOPS. Gesunde Pflanz 70:10343-018 (2018).

AdV, Dokumentation zur Modellierung der Geoinformationen des amtlichen Vermessungswesens (GeoInfoDok): Erläuterungen zum ATKIS® Basis-DLM, Version 6.0.1, Stand 25.08.2015 (2015).

AdV, Dokumentation zur Modellierung der Geoinformationen des amtlichen Versuchswesens (GeoInfoDok): ATKIS-Objektartenkatalog Basis-DLM, Version 6.0, Stand 11.04.2008 (2008).

Verro R, Finizio A, Otto S and Vighi M, Predicting pesticide environmental risk in intensive agricultural areas. II: Screening level risk assessment of complex mixtures in surface waters. Environ Sci Technol 43:530–537 (2009).

Zhan Y and Zhang M, PURE: a web-based decision support system to evaluate pesticide environmental risk for sustainable pest management practices in California. Ecotoxicol Environ Saf 82:104–113 (2012).

model inputs

geospatial data of agricultural fields in in a considered region
surface waters
topography
oil characteristics
weather data
pesticide use data
Information on plant protection products (active substances, concentrations, labelled mitigation measures)
physico-chemical properties of the active substances were obtained from the Pesticides Properties Database

model outputs

Acute aquatic risk to aquatic non-target-organisms
Acute aquatic risk to aquatic non-target-organisms
Acute risk to non-target-organisms in the field margi
Chronic risk to soil organisms

Intended field of application

policy role: Not provided

policy areas

Agriculture and rural development

Model transparency and quality assurance

Are uncertainties accounted for in your simulations?: NO - too demanding

Has the model undergone sensitivity analysis?: YES

Has the model been published in peer review articles?: YES

Has the model formally undergone scientific review by a panel of international experts?: NO

Has model validation been done? Have model predictions been confronted with observed data (ex-post)?: YES - For a small catchment and a small set of active ingredients

To what extent do input data come from publicly available sources?: Entirely based on publicly available sources

Is the full model database as such available to external users?: YES - Weather data, phenology data, soil data, the product database and active ingredient data can be provided by public web services. All web-services are based on public available data. Additional data can be requested by e-mail: sf@julius-kuehn.de

Have model results been presented in publicly available reports?: YES

Have output datasets been made publicly available?: YES

Is there any user friendly interface presenting model results that is accessible to the public?: YES - Not for the regional assessment, but for field specific risk assessment.

Has the model been documented in a publicly available dedicated report or a manual?: NO

Intellectual property rights

Licence type: Free Software licence

application to the impact assessment

Please note that in the annex 4 of the impact assessment report, the general description of the model (available in MIDAS) has to be complemented with the specific information on how the model has been applied in the impact assessment.

See Better Regulation Toolbox, tool #11 Format of the impact assessment report).