Main authors: Susanne Klages, Nicolas Surdyk, Christophoros Christophoridis, Birgitte Hansen, Claudia Heidecke, Abel Henriot, Hyojin Kim, Sonja Schimmelpfennig
FAIRWAYiS Editor: Jane Brandt
Source document: »Klages, S. et al. 2018. Review report of Agri-Drinking Water quality Indicators and IT/sensor techniques, on farm level, study site and drinking water source. FAIRWAY Project Deliverable 3.1, 180 pp


Contents table
1. The process of prioritisation of indicators
2. Survey of ADWIs already used in the FAIRWAY case studies 
3. First step in prioritistion of indicators in FAIRWAY

In this article, an overview on principles and aims of a priorisation process is given , followed by a summary on the outcome of a survey among FAIRWAY case studies on indicators used and an explanation of the stepwise priorisation process chosen in FAIRWAY.

1. The process of prioritisation of indicators

The absence of a properly documented indicator selection process is not a minor issue: Niemeijer and de Groot (2008) explain, that the choice of indicators highly influences conclusions as to whether environmental problems are serious or not, whether conditions are improving or degrading, and in which direction causes and solutions need to be sought. The authors propose to use the enhanced DPSIR-framework to frame the indicator-selection: causal chains are linked to form a causel network, similar to a flowchart. These are according to Niemeijer and de Groot (2008) the steps to steps to build a casual network:

  1. Broadly define the domain of interest.
  2. Determine boundary conditions that can help determine which aspects to cover and which to omit.
  3. Determine the boundaries of the system.
  4. Identify (abstract) indicators covering the factors and processes involved.
  5. Iteratively map the involved indicators in a directional graph.

Figure 8.1. shows the ideal process for indicator selection.

D3.1 fig08.1
Figure 8.1

The following elements and criteria are of relevance for the process:


Contextualisation describes the preliminary choices and assumptions (Bockstaller et al., 2008) and includes a definition of the purpose of the analysis, the desired level of operation (farm, region, member state…), the temporal analysis scales and also the involvement of stakeholders (Lebacq et al., 2013).

Agricultural relevance

According to CORPEN (2006), indicators for nitrate pollution that describe or estimate the condition of a plot (e. g. soil cover, nitrogen budgets and model-derived indicators) are more relevant than the indicators that only describe fertilisation practices (e. g. phased fertilisation). Indicators of high relevance should be preferred. Indicators of low relevance should be used only as part of a set. These sets are, however, difficult to interpret: the larger the number of indicators, the more likely they give divergent assessments. To avoid this, single indicators can be combined within a chart or an index. Annex 6 in »FAIRWAY Project Deliverable 3.1 shows estimates on plot level of the relevance of a range of indicators evaluating the potential of nitrates pollution of ground- and surface waters.

Data availability within case studies/official statistics

Often, limitation of data availability compelled data driven approaches to focus on agricultural practices and hence on means-based indicators. Model-based and effect-based data indicators require context-specific data, e. g. climate and soil characteristics or specific on-site measurements, that are for some reason not available. A solution may be the use average data as default values, for a region or a sector (Lebacq et al., 2013).


With increasing scale, direct methods are getting too expensive and are replaced by indirect methods. Annex 7 in »FAIRWAY Project Deliverable 3.1 shows for plot, farm and regional level how the feasibility of indicators for the potential of nitrate pollution of ground- and surface waters was evaluated for France by CORPEN (2006).

According to Lebacq et al. (2013), criteria for a prioritisation of indicators can be summarised under the main categories

  • relevance,
  • practicability and
  • end user value.

In data-driven approaches, most means-based indicators and some intermediate indicators, such as nutrient surplus, can be used to assess environmental themes, because they are based on farmers’ practices. As means-based indicators often posess a low quality of prediction of envirnmental impacts, in order to increase accuracy, Bockstaller et al. (2008) propose to use a combination of indicators for the same theme. This procedure may be complicated in practice and requires an aggregation process but allows to focus on significant variables to develop simplified indicators.

In order to be able to compare indicators, functional units are applied (Thomassen et al. 2008):

  • the expression of impacts per amount of product (i.e. liter of milk, kilogram of meat) is related to the function of market goods production,
  • the expression per hectare of agricultural land refers to the function of non-market goods production, such as environmental services (Basset-Mens and van der Werf 2005).

Indicators concerning global impacts, e. g., greenhouse gas emissions, should be expressed per unit of product, while indicators related to local impacts, e. g., eutrophication potential, should be expressed per hectare (Halberg et al. 2005a).

Indicators differ with respect to the compartments considered (i.e. soil, surface water, groundwater and air) and effects taken into account. Therefore, the results obtained can strongly depend on these factors (Oliver et al., 2016). The evaluation of indicators should consider multiple applications and wide range of applicability. They should also take into account the synergistic effects of applying different pollutants (e. g. pesticides) and they should consider the application method and the level of application (regional, field scale).

2. Survey of ADWIs already used in the FAIRWAY case studies

The aim of this section of FAIRWAY is to to prioritise and evaluate data-driven indicators for the monitoring of the impact of agriculture activities on nitrates and pesticides in drinking water.

A questionnaire on ADWIs already in use was compiled and sent to all FAIRWAY case studies. Case study leaders were asked to choose out of a set list those indicators for drinking water pollution by nitrates and pesticides used in their case study. They were also asked to indicate the level (plot, farm, regional or higher) on which data for the calculation of these indicators are available. Case study leaders were also asked for further suggestions on ADWIs. The results of this survey are enclosed in this report as Annex 2 in »FAIRWAY Project Deliverable 3.1 and also available as »Milestone 3.1 from the FAIRWAY website.

Main results of this survey are as follows:

  • The aim, size and structure of the different case studies are different, and so are the ADWIs in use.
  • Those case studies with focus on nitrate pollution do not dispose pesticide indicators and vice versa.
  • ADWIs and the data to calculate them may be available on plot, farm or regional level.
  • There are far more indicators and data in use which are related to nitrogen than to pesticides.
  • Indicators in use for pesticide pollution are combined/compound indicators.

Questions on confidentiality of farm data aroused in conjunction with the survey. This is due to uncertainties related to the new regulation on data protection (EU 2016/679), but also due to a tightening of fertiliser legislation in some Member States.

3. First step in prioritistion of indicators in FAIRWAY

From the other articles in this section of FAIRWAYiS, the following aspects for a further prioritisation of ADWI can be deduced:

  • ADWI are useful on all levels: at farm level as an aid in farmer’s consultation, at local or even national level as an evaluation and monitoring tool for administration work and for policy-makers.
  • Regarding the two kinds of pollutants – nitrate and pesticides – frame conditions are quite different:
    - Nitrate is one single substance, being mobilised and immobilised, leached, transported by runoff and emitted. It is essential for plant growth and omnipresent, even under “natural” conditions.
    - On the contrary, around 250 so called “active substances” of pesticides are authorised by EFSA. Placement on the market of pesticide product needs national approvement. They may only consist of the registered active substances registered on EU-level, pure or in mixture, and of additives, for a better handling of the pesticide. Pesticides are supposed to be – to the greatest possible extent - harmless. They are supposed to degrade or at least to be absorbed by the soil matrix, but not to leach into groundwaters. Improper handling may however lead to runoff or drift and therefore to pollution of surface waters.

In »Agri-drinking water quality indicators at farm and drinking water levels, possible ADWIs are listed and explained. The ADWIs include those being subject of the survey among the case studies, including those indicators the case study leaders were proposing to be included in a further evaluation. Additionally, indicators used for pesticide monitoring/risk assessment were included, the range of pesticide indicators used in case studies was limited (see Annex 2 in »FAIRWAY Project Deliverable 3.1).

From the number of indicators listed, it can be deduced that indicators which act in the agricultural sector as driving forces and as pressure indicators, are far more numerous than state respectively impact indicators. In this sense, the relation being visualised in for AEI related to water quality on European level is mirrored for the frame conditons of the FAIRWAY project. The large number of driving forces and pressure indicators which stand for agricultural activity also explains, that from this part of the DPSLIR-model, many factors may influence water pollution. State indicators which are used for the evaluation of the water quality are on the contrary far more standardised, like the water quality standards they are supposed to monitor.

We have introduced the new concept of Link indicators within the DPSLIR-model, in order to explain the time lag between agricultural activity and water pollution and to elicit, which farm management practices would at all lead to water pollution.

A prioritisation of ADWI is therefore above all necessary for the driving forces and pressure indicators in the agricultural sector, in order to focus on the most

  • significant,
  • prevalent
  • effective and
  • easy to use indicators.

The survey on ADWIs already used in case studies and the most promising indicators discussed in »Agri-drinking water quality indicators at farm and drinking water levels lead to a first weighting of indicators. The result is listed in Table 8.1. On the right part of the table, three columns were added, which show the evaluation of a survey among FAIRWAY case studies about data availability in order to calculate ADWIs. Answers would also indicate the resolution in space, in which data can be delivered from the case studies (at plot, farm or regional/larger scale).

Table 8.1: Ranking of ADWI according to significance and prevalence based on a survey carried out in FAIRWAY

Subindicator of ADWIs Prevalance: evaluation of data availability in case studies (number of times mentioned)
  Plot scale Farm scale Regional scale
Land use/land cover 6 2 5
Land use change      
Precipitation/evapotranspiration 2 2 12
Soil type 5 1 4
Organic carbon      
Organic/conventional 1 7 1
(Average) crop yield  1 7  1
Cropping patterns      
Method of soil cultivation/tillage practice      
Soil cover      
Livestock density (LU/ha /yr on an area of reference) 3 7 4
Livestock excretion (kg N/ha/yr on an area of reference) 1 5 1
Organic fertilisation/ha; organic fertilisation/crop*ha 2 6 0
Mineral fertilisation/ha; mineral fertilisation/crop*ha 4 4 6
Total fertilisation/ha; total fertilisation/crop*ha 2 7 2
Type of Pesticides      
Chemical properties      
Consumption of pesticides      
Application of pesticides/ha (active substances; frequently used; most persistent or toxic) 2 6 0
Application of pesticides/ha*crop (active substances; frequently used; most persistent or toxic)      
Timing of pesticide application      
Splitting/frequency of pesticide application      
Nitrates in soil water 4 1 2
Pesticides in soil water      
Nitrogen leaching risk indicators      
Pesticide leaching risk indicators      
Surface transport of nitrogen and pesticides (with soil/fertiliser particles)      
Pesticide drift      
Volatile N-compounds      
Nitrate: grazing animals near surface waters, farmyard, storage facilities      
Pesticides: farmyard, pesticide storage facilities      
Annual average nitrate concentration (mg NO3/l) 4 1 8
Concentration trend analysis      
Frequency of exceedance quality standards (%) 2 0 8
Nitrogen maximal concentration in drinking water collection points 3 0 8
Catchment typology and dominant flowpath      
N stable isotopes      
Number of substances that exceed water quality standards at least once the year 4 0 7
Maximum concentration by substance (if >0.1 µg/l) in drinking water collection points 4 0 7
Frequency of exceedance quality standards in the drinking water (percentage of the number of samples where the 'drinking water' standard is exceeded) by substance 4 0 6
Vulnerability assessment maps of aquifer and surface water 2 0 7

In Table 8.1, ADWI, for which data can be supplied by the case studies are marked in orange. ADWI for which data can (possibly) not be supplied by case studies are marked in blue. This may be the case because these data are not used in certain or all case studies, or because in the data survey carried out in the beginning of FAIRWAY, we did not ask for the specific information. This applies to background information (e. g. climate, topography, rock types) about the case study sites, which may be critical for leaching risk assessment and catchment typology; therefore, in the data compliation stage, we will collect such data as well. It also refers to specific information particularly on pesticide use. Case studies do not seem to collect specific data on the use of single active substances. But from sum parameters and general indices, no link can be drawn to the parameter at sink level (e. g. pesticide analyses of raw water).

Indicators, for which data are not readily available in the case studies may be calculated if these data are free available from other data sources. Annex 3 in »FAIRWAY Project Deliverable 3.1 lists data sources for free available data in order to calculate ADWIs. One example is the use of pesticides, which may be deduced from local cropping patterns and from usage data reported from the Member States according to Regulation (EC) No 1185/2009. The next step towards priorisation will be done in FAIRWAY using data of cathments in the case studies (see »Further prioritisation and evaluation of agri-drinking water quality indicators). For this reason, data are requested from the case studies.


Note: For full references to papers quoted in this article see

» References


Go To Top