Agri-drinking water quality indicators and IT/sensor techniques

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

 

Results from this research task have also been published as scientific papers:

  • Klages, S.; Heidecke, C.; Osterburg, B. The Impact of Agricultural Production and Policy on Water Quality during the Dry Year 2018, a Case Study from Germany. Water 2020, 12, 1519. https://www.mdpi.com/2073-4441/12/6/1519
  • Klages, S.; Heidecke, C.; Osterburg, B.; Bailey, J.; Calciu, I.; Casey, C.; Dalgaard, T.; Frick, H.; Glavan, M.; D’Haene, K.; Hofman, G.; Leitão, I.A.; Surdyk, N.; Verloop, K.; Velthof, G. Nitrogen Surplus—A Unified Indicator for Water Pollution in Europe? Water 2020, 12, 1197. https://www.mdpi.com/2073-4441/12/4/1197

Nitrogen and pesticide cycles in the agri-hydrogeochemical system

We start this section by defining the agri-hydrogeochemical system and looking at the pathways that nitrates and pesticides follow from the agricultural system to the drinking water supplies. We consider the challenges in monitoring and regulation, particularly of pesticides, and how contaminated water is treated in water works.
»Nitrogen and pesticide cycles in the agri-hydrogeochemical system

Data and indicators to regulate and monitor the use of nitrates and pesticides

We then look at what data and statistics there are available on the regulation, marketing and use of nitrogen and pesticides, what indicators are used to monitor them and how indicators are intended intended to support central and local administration and policy-makers, water companies in analysing the situation of diffuse pollution and selecting measures to protect drinking water resources.
»Data and indicators to regulate and monitor the use of nitrates and pesticides

Developing FAIRWAY agri-drinking water quality indicators (ADWIs)

The DPSIR model is defined as “causal framework for the description of interactions between society and the environment”. It was adopted by the European Environment Agency (EEA 2018). According to its terminology, social and economic developments (Driving forces, D), exert Pressures (P) on the environment and, as a consequence, the State (S) of the environment changes. This leads to Impacts (I) on ecosystems, human health and society, which may elicit a societal Response (R) that feeds back on Driving forces, on State or on Impacts via various mitigations, adaptations or curative actions (Smeets and Weterings, 1999; Gabrielsen and Bosch, 2003). In FAIRWAY we consider ADWIs within the DPSIR-framework. The adjusted DPSLIR-framework contains a new element, the Link Indicator.
»Developing FAIRWAY agri-drinking water quality indicators (ADWIs)

Agri-drinking water quality indicators at farm and drinking water levels

Agri-environmental indicators (AEI), as developed by OECD and Eurostat, are implemented and further developed for the monitoring and evaluation of the negative and positive impacts of agricultural activities on the environment. AEIs are used on European/national level (28 AEI are listed in fact sheets related to COM final 0508/2006 (Eurostat, 2018). The AEI are applied e. g. to evaluate/benchmark the transcript of EU-legislation at Member State level), at regional level (to monitor the impact of agriculture on environment, identify hotspots or focus subjects and areas for the agricultural advisory service) and at farm level (as decision aid tool for the farmer). Agri-drinking water quality indicators (ADWIs) to be developed in FAIRWAY are defined as indicators for the quality of drinking water. As drinking water may be produced from groundwater or surface water, ADWIs aim at the quality of both. As done for the 28 harmonised AEI (COM 2006, Eurostat 2018), we classified all ADWIs, which the case studies reported into the adjusted DPSLIR framework. We added further ADWIs according to a literature review. The ADWIs listed in the table may work as indicators by themselves or they are elements of compound indicators. Indicators for both, nitrates and pesticides, are listed in the same table, in order to avoid redundance as far as possible.
»Agri-drinking water quality indicators at farm and drinking water levels

Prioritisation of agri-drinking water quality indicators

All the ADWIs that are the subject of the survey among the case studies, those proposed by the case study leaders to be included in a further evaluation and those which, according to a literature review, are used for pesticide and nitrate monitoring/risk assessment are listed and described. Indicators which act in the agricultural sector as Driving forces and as Pressure indicators, are far more numerous than State respectively Impact indicators. The large number of agricultural Driving forces and Pressure ADWIs 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. 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 leads to a first weighting of indicators.
»Prioritisation of agri-drinking water quality indicators

Further prioritisation and evaluation of agri-drinking water quality indicators

In order to further drive forward the proiritisation of the selected ADWIs in FAIRWAY, we intend to connect ADWIs from the agricultural and the water work side, using statistical methods. We also intend to further investigate on the Link indicator, especially how this ADWI fits in between the other indicators. We intend to examine

  • the feasibility of indicators calculation,
  • the link between indicators, and
  • the relevance of some indicators, as statistical calculations give the mathematical expression for the link that exists between them.

For this purpose, a database of ADWI-data on catchments-level will be established by collecting data from the FAIRWAY-case studies. Preparatory work has been carried out, using the Voulzie case study, in order to specify the data request to the case studies. Statistical analyses of data of the Voulzie case study showed, that the spring discharge time series can be rather well explained by the evolution of the recharge of the year before. The first attempt to build this database enabled the calculations of indicators as well as the first links between Pressure indicators and State indicators. Finding the proper, statistically based link between agricultural Driving forces and Pressure indicators and the State/impact indicators might supply ADWIs on a reliable basis.
»Further prioritisation and evaluation of agri-drinking water quality indicators

IT/sensor and automatic sampler techniques for pesticide and nitrate sampling

Monitoring has evolved considerably over the past ten years and even more in recent years. There are broad avenues for innovation and, as part of the FAIRWAY project, a review of in situ monitoring methods has been achieved, in accordance with the chapter on participatory monitoring. Many methods can also be applied in the laboratory. A review showed that many tools (some are prototypes) and methods are being developed to improve measures for both nitrates and pesticides. The developed methods are based e. g. on optical sensors and paper based sensors. These tools make it possible to improve the confidence in the measurement while improving the analytic capacities of the devices (limits of measurements and types of molecules). In addition, relays with smartphones can be developed to facilitate the reading of the results and to trust them.
»IT/sensor and automatic sampler techniques for pesticide and nitrate sampling

Participatory monitoring: involvement of citizens

Participatory monitoring, although old in its concept, has become much more developed during the last decades. Several types of participative monitoring systems can be characterised in relation to the intended goal of the promotor. Participatory monitoring initiatives can often be considered successful as they allow measurement of phenomena at frequencies and locations that are not reachable by a team of researchers alone. On the other hand, associated difficulties have been identified. First, it is not always easy to find the right number of participants to complete a large program, some "site-specific" programs may be canceled due to lack of participants. Moreover, in our field of water and environment, participatory programs can only hope to change behaviors if educational tasks have been planned in the projects. Lastly, participatory monitoring programs generally only work with a coherent method to analyse the data (computer infrastructure and/or scientific manpower) that must be anticipated. If the educational tasks and IT tasks are taken into account, participatory monitoring programs are not necessary less expensive than the institutional programs. The review of in situ monitoring tools in development (even prototypical) suggests possibilities of access to increasingly simple and robust tools or new probes attached to smartphones. Thanks to these tools, some problems, such as the lack of participants and some analysis bias, could be resolved.
»Participative monitoring: involvement of citizens

Conclusions

From a survey among the FAIRWAY case studies on indicator use and from the the information in this section of FAIRWAYiS, the following aspects can be deduced:

  • Regarding the two kinds of pollutants – nitrates and pesticides – the framing 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.
    - In contrast, 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.
  • 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. However, as more aggregated data show less standard deviation than the single datasets, correlation of ADWI with water quality could be stronger between data on a regional level than on farm level.
  • ADWIs which act in the agricultural sector as Driving forces and as Pressure indicators are far more numerous than State or Impact indicators; this indicates how many factors from the agricultural side 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.
  • Aim, size and structure of the different case studies are different, and so are the ADWIs in use. very few ADWIs are uniformly used throughout Europe.
    - Common indicators on nitrate risk in use are rather simple statistics on fertiliser use, animal density or yield, but also N-budgets are applied.
    - Pesticide risk indicators in use are compound/composite indicators, like the Treatment Frequency Index and Pesticide Load Index.
  • Concerning pesticides, the DPSLIR-model can only be used, if data on the Driving force and Pressure side on the use of specific pesticides are available and can be linked to the State/Impact side. Since a regional differentiated data compilation of application data and a consequential estimation of the pesticide inputs is missing, pesticides found in drinking water can only sporadically be related to application data (SRU, 2016).
  • Correlation analysis with data of the testsite showed, that the compound/composite indicators (field budget or Cassis-N surplus) were not the ones with the best correlation: budgets calculate N-losses from the root zone, and therefore do not take into account the N-losses in the unsaturated zone beneath the root zone (this is the reason why we introduce the Link indicator for the DPSLIR-framework). Composite indicators may show a low relative sensitivity for changing conditions (Buczko and Kuchenbuch, 2010).
  • Calibration and validation of ADWIs against field data is of high importance (Buczko and Kuchenbuch, 2010a).
  • The data acquisition scale may be a problem, because readily available data categories at the national level are difficult to access at the local level. 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, questions on confidentiality of farm data arise in conjunction with the survey.

 


Related articles

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. Definitions of the agri-hydrogeochemical system
2. Pathways in the hydrogeochemical system
3. Nitrogen cycle in the agri-hydrogeochemical system
4. Pesticide cycle in the agri-hydrogeochemical system
5. Nitrate and pesticides in the waterworks system

1. Definitions of the agri-hydrogeochemical system

Agri-drinking water quality indicators (ADWIs) are selected within the cycles of nitrate and pesticides in the agri-hydrogeochemical system. The drivning force (D), pressure (P), state (S), and link (L) indicators are defined in the agri-hydrogeochemical system while impact (I) and response (R) are considered to be outside the system (see Figure 3.1).

D3.1 fig03.1
Figure 3.1

The agricultural system, in the ADWI context, is physically defined by the zone between the atmosphere and the rooting depth where all the agricultural activities and reactions occur (Figure 3.1). In the agricultural system, interplay between human activities (e. g. fertiliser and pesticide use, crop production) and natural processes (e. g. nitrogen cycle, soil erosion, adsorption/desorption, denitrification) control leaching of nitrate and pesticides to the underlying hydrogeochemical system. The driving force (D) and pressure (P) indicators represent the agricultural system and the primary focus is to quantify the leaching and run off of nitrates and pesticides into the underlying hydrogeochemical system.

The hydrogeochemical system is the zone from the ground surface to the drinking water abstraction point (Figure 3.1). The hydrogeochemical system governs pathways to deliver nitrate and pesticide from the agricultural system to the drinking water abstraction point. The pathways control the transit time of pollutants, i. e. nitrate and pesticides, and biogeochemical reactions that may change the concentrations and phase of them in the hydrogeochemical system (Figure 3.1). The state (S), and link (L) indicators describe the fate, retention and transport of nitrate and pesticides in the hydrogeological system. The waterworks system describes drinking water production processes (Figure 3.1).

In the waterworks system, nitrate and pesticides in raw waters – groundwater or/and surface water – might be removed by various types and degrees of processes, depending on the water quality and technological possibilitites. The state (S) indicator shows the quality of drinking water.

2. Pathways in the hydrogeochemical system

Pathways in the hydrogeochemical system are the routes of nitrate and pesticides from the agriculture system to drinking water abstraction points. Identifying the dominant pathways are important for different reasons related to:

  1. Planning and selection of specific agricultural mitigation measures in regard to protection of water ressources as lakes, streams or groundwater taking the lack time into account,
  2. Planning and selection of drinking water protection strategies and treatment possibilities in order to secure clean drinking water in the short and long term perspective.

Therefore, one of the key roles of the ADWIs should be identifying the dominant pathways of the hydrogeochemical system. Dominant pathways of the hydrogeochemical system are controlled by complex interplay between its hydrogeologic structure (e. g. soil type, soil thickness, soil moisture, surface topography, bedrock type, groundwater table depth, hydrogeology and hydraulic parameters) and climatic conditions (e. g. seasonality, rainfall intensity); therefore, it may spatially vary and seasonally shift.

In the context of development of ADWI, we conceptualised the pathways as follows: two pathways for groundwater and four pathways for surface waters (Table 3.1).

Two pathways for groundwater: To recharge groundwater, water primarily flows vertically via

  1. Matrix flow pathways and/or
  2. Preferential flow pathways (Figure 3.1).

This water eventually emerges back to the surface water.

Matrix flow is a pathway through pore spaces in the soil matrix. In the unsaturated zone, matrix flow moves uniformly with a wetting front, therefore it is also called as uniform flow. The transit time of matrix flow can be long (months to years; Table 3.1); therefore, the groundwater table and groundwater chemistry show relatively small variations and slow changes over time.

Preferential flow is a pathway via macro-pores in the soil and fractures in bedrock, bypassing a dense or less permeable matrix (Beven and Germann, 1982, Hendrickx and Flury, 2001). The macro porous spaces in soils can be created along root channels, soil fauna channels, cracks (i. e. freeze-thaw, wetting-drying), fissure, or soil pipes (Beven and Germann, 1982). Preferential flow may be transiently active; however, it can deliver a significant quantity of contaminants to groundwater quickly (hours ~ weeks; Table 3.1).

Four pathways for surface waters: Horizontal flow is the most dominant pathways for surface waters govering the transport and fate of contaminant in the systems. The horizontal flow pathways are

  1. Overland flow,
  2. Interflow,
  3. Groundwater discharge and
  4. Tile-drainage flow (Figure 3.1).

Overland flow is water flowing along the land surface directly into the stream. This occurs in some landscapes, where the groundwater table is near the land surface (peat soils) or the top soil is extremely impermeable e. g., clay rich (Figure 3.1). The transit time of overland flow is extremely short (Table 3.1) and the water will be continuously exposed to fertilisers or pesticides along the pathways. Furthermore, overland flow causes soil erosion, which may transport nitrogen and pesticides in the particle phase. Therefore, a hydrogeologic system with overland flow is expected to be highly dynamic and vulnerable to contamination.

Interflow occurs in the unsaturated zone where infiltrated water flow laterally via preferential pathways and travel directly to the stream (Figure 3.1). Interflow may develop a localised and transiently saturated zone and water may flow relatively fast (e g., days to weeks; Table 3.1).

Groundwater discharge is laterally groundwater flowing directly into surface waters such as streams and lakes. The transit time of groundwater to surface waters may vary but, among the four pathways to surface water, it is the slowest pathway (Table 3.1). Therefore, in a groundwater-dominated system, stream discharge and chemistry may response to the rainfall inputs slowly.

Tile-drainage may be another important pathway in clay rich soils. It may operate in a similar manner to that of interflow (Table 3.1).

Table 3.1: Definitions and qualitative transit time of pathways

Water resource Pathways Definition Transit time
Surface water Overland flow pathways along the land surface very short (~hours*)
  Interflow pathways through the unsaturated (or partially saturated) subsurface, mainly via soil macro-pores, fractures, or perched groundwater short (days~weeks*)
  Tile drains pathways via tile drains short (days ~weeks*)
  Groundwater flow pathways through the saturated zone long (years~decades)
Groundwater Preferential flow Flow paths via fractures and macro-pores short (hours~weeks)
  Matrix flow Flow paths via matrix (i.e. via pore spaces) long (years~decades)

*) These time specifications are approximations to compare the paths to each other. These approximations are only valid when the flow of water is sufficient during wet conditions/heavy rainfall. This is not always the case. The transit time is dependent on the water flows (in this case rainfall).

3. Nitrogen cycle in the agri-hydrogeochemical system

Input and output of N in the agri-hydrogeochemical system

N is introduced to the agri-hydrogeochemical system as fertilisers (mineral and organic fertilisers), atmospheric deposition, and biological N fixation. N is removed from the agri-hydrogeochemical system by crop and animal production, manure export, and biological denitrification (Figure 3.2).

D3.1 fig03.2
Figure 3.2

Fate of N in the agri-hydrogeochemical system

N is present in soils in both mineral (e. g. nitrate or ammonium,) and organic forms (e. g. urea, crop residue, manure, soil organic matters). Some organic fertilisers (like urea) rapidly hydrolyze into ammonium after application and are therefore sometime consideded as mineral fertiliser. The total N stock in soil (Nt) is almost equal to organic N. Mineral N (Nm) is readily soluble and bioavailable, and so it can be taken up by crop. There are, however, a range of situations where the crops can not absorb all nitrogen. This may enhance leaching of nitrogen.

Organic N must be mineralised to NO3- or NH4+ first via microbial organisms to be utilised by plants and transported with water (Figure 3.2). Rates of microbial N mineralisation may depend on environmental conditions (e. g. climate) and types of organic N. For example, the mineralisation rate is higher in warm and humid climate conditions. Liquid manure with a narrow carbon/nitrogen ratio results in higher mineralisation rates than farm yard manure containing straw.

Mineral N can also be immobilised by soil micro-organisms (Figure 3.2). For example, immobilisation of mineral N occurs when cerial straw (high C/N ratio) is incorporated into the soil after harvest.

Denitrification reactions are the main pathways to remove nitrate from the agri-hydrogeochemical system as gaseous N, i. e. dinitrogen (N2), nitrous oxide (N2O), nitrogen oxides (NOx). Types and rates of microbial N denitrification reactions depend on available energy source (e. g. organic carbon, pyrite) and the redox condition. Denitrification reactions occur only under the redox condition (no oxygen present). Such conditions develop mainly in the root zone layer and deep saturated zone. Denitrification also occur in the upper part of the root zone during wet condtions (e. g. heavy rainfall).

In the soil layer, microorganisms reduce nitrate by oxidising organic carbon (Figure 3.2). In this layer, the influx of fresh organic matters from the ground surface fuel the microorganisms and the reduced condition may develop at a micro-scale.

In the reduced saturated zone, in contrast, organic matter usually is less available to fuel the microorganisms. In this layer, microorganisms use different energy sources. A coupled pyrite oxidation and nitrate reduction is one of the well-known reactions to remove nitrate in the deep saturated zone (e. g. Figure 3.2; Postma et al., 1991).

In the unsaturated zone and the oxic saturated zone, nitrate travels conservatively. In the unsaturated zone, the soil air usually contains oxygen, and the oxic groundwater is defined by the presence of detectable dissolve oxygen. Therefore, nitrate concentrations in water in the unsaturated zone and the oxic groundwater may show the similar level to that of water leached out of the root zone.

Pathways of N in the hydrogeochemical system

Nitrates travel vertically via matrix flow and/or preferential flow in the hydrogeochemical system (Figure 3.2). For groundwater, nitrate concentrations will vary with depth. Despite intense denitrifying activities in the root zone, nitrate concentrations will be highest in the root zone due to high N input. In the unsaturated zone and oxic groundwater, nitrate concentrations in water may be similar to the concentrations in the leaching from the root zone. NO3 concentrations becomes negligible in the reduced saturated zone.

In the saturated zone or groundwater nitrate reduction occurs in a transition zone with anoxic condition often called the redox interface. Here the nitrate concentrations are lower than the concentrations in the leaching from the root zone and often nitrite is used as an indicator anoxic nitrate reducing condtions.

For surface water, nitrate concentrations will vary depending on the dominant pathways. The pathways through the zone between the soil surface and oxic groundwater – overlandflow, interflow, tile-drainage, and oxic groundwater – will deliver nitrate while the reduced groundwater will not be a pathway of nitrate to the surface water (Figure 3.2).

Reasons for N leaching/runoff from the agricultural system

An important reason for the leaching of nitrates below the root zone is the fact, that in majority of soils most of the soil nitrogen (Nt) is organically bound as soil biomass (humus) and slowly released over time by microbial degradation and transformation into mineral nitrogen (Nmin). Only the microbial biomass itself is fast degradable (Nfast) (Beisecker et al., 2015). The release rate of the organically bound nitrogen is linked to the microbial activity in soil: it is high under warm and humid climate conditions and low during cold and dry weather. Small changes in the climatic frame conditions lead to a relatively large alteration of the N release rate. The larger the Nt-stock in soil, the less predictable the amount of N which will mineralise during one growth season (Figure 3.3). With advancing climate change, the situation may even become worse as climate conditions become less predictable.

D3.1 fig03.3
Figure 3.3
D3.1 fig03.4
Figure 3.4

N-fertiliser planning usually takes into account a certain amount of Nmin in soil at the beginning of the vegetation period and a certain amount of mineralised nitrogen during the plant growth. The difference between plant need and this soil borne nitrogen should be met by N fertilisation (Figure 3.4). Nitrates leach out of the agricultural system due to the unpredictablility of both, the plant development during the growing season and and of the amount of nitrogen which will be mineralised.

A reasonable efficiency of N fertilisation (quota of N in harvested field products versus the amount of fertilised N) is around 50 to 60 % under central European conditions, which means around 40 to 50 % of the applied nitrogen is not harvested as crop product. In Denmark the N use efficiency in 2014 was around 40 % (Hansen et al, 2017). Besides the above explained factors this is due to the following reasons:

  • Ammonia losses, especially if fertiliser application techniques are used which do not insert nitrogen fertilisers into the soil.
  • Denitrification losses from the top soil, e. g. after application of nitrate containing fertilisers to soil rich in organic matter (high denitrifcation potential), such as grasslands and peat soils.
  • Plant growth/development and in consequence N plant need is not exactly predictable, as it depends on a range of factors (e.g., climate, plant health etc.).
  • An imbalance between nitrogen demand and supply: this may be absolute, in case nitrogen (as mineral fertiliser or manure) is applied under positive yield estimates.
  • The imbalance may be relative, when nitrogen supply and plant demand do not match in course of time (due to N mineralisation or not appropriate timing of N fertilisation).
  • The harvested crop contains not all fertilised N, part of it remains in roots and plant parts which remain on the field (i.e. straw, leaves).
  • The type of organic fertiliser used: liquid manure or digestate with a narrow carbon/nitrogen ratio posess a higher N-release rates than solid organic fertilisers, such as compost (Gebauer and Schaaf, 2017).
  • Point sources, e. g. grazing animals with access to a small stream or lake, may cause nitrogen pollution to surface waters (Bohner et al., 2007).

4. Pesticide cycle in the agri-hydrogeochemical system

Inputs and outputs of pesticide to/from the agri-hydrogeochemical system

Pesticides are introduced to the agri-hydrogeochemical system by pesticide application, atmospheric deposition and drift (Figure 3.5). Pesticides can be removed from the agri-hydrogeochemical system by crop production (accumulation in crops); however, removal by crops may be a minor loss compared to the overall pesticide cycle (Figure 3.5).

D3.1 fig03.5
Figure 3.5
D3.1 fig03.6
Figure 3.6

Fate of pesticides in the agricultural system

There are approximately 250 active substances approved by the EFSA for use in the European Commission (2018a) and these substances show a wide range of physical, chemical, and biological properties. Nitrogen, for instance, is transported predominant as nitrate in water. Pesticides, on the contrary, are transported as gas, particles, and solutes. To a less extent, pesticides can be transported via biota. In addition, pesticides undergo more complex physicochemical and biogeochemical transformation than nitrogen does.

Here, we provide a general overview of the pesticide cycle in the agri-hydrogeochemical system from the ADWI perspectives. We focus on two properties of pesticides that may control the appearance of pesticides in water: persistence (or degradability) and mobility.

The fate of pesticides is controlled by three types of processes: transfer, transport, and degradation processes (Figure 3.6: Fent 2005; Gavrilescu, 2005). Via transfer processes, pesticides move among different environmental media such as air, soil, water and biota. In each medium, pesticides undergo different degradation processes. Transfer processes are responsible for moving pesticides from the initial sources. Persistence and mobility of a pesticide are governed by interactions between the pesticide’s property and these processes.

Persistence. Persistence is degradability of a pesticide by transformation and degradation processes. Via these processes, the structure of a pesticide breaks down and its toxicity usually decreases. The physical and chemical characteristics of pesticides may be the first order control for their degradability. In general, more reactive ones (e. g., soluble, small-sized compound, aliphatic structure) are more easily degradable. Table 3.2 summarises the pesticides properties that may control the degradability of organic pesticides (Gavrilescu, 2005).

Table 3.2: Physical, chemical and structural characteristic that may control degradability of organic pesticides (modification of Table 5 from Gavrilescu, 2005)

Properties Degradability
  More easily Less easily
Solubility in water Soluble in water Insoluble in water
Size Relatively small Relatively large
Functional group substitutions Fewer functional group Many functional groups
Compound more oxidized In reduced environment In oxidized environment
Compound more reduced In oxidized environment In reduced environment
Created Biologically Chemically by man
Structure Aliphatics (branch structure) Polyaromatic (ring structure)

The degradation processes are divided into three categories: microbial degradation, chemical degradation, and photodegradation (Gavrilescu, 2005).

Microbial degradation is the primary process to degrade pesticides in soil and water. Soil biota, such as microorganisms, bacteria and fungi may use pesticides as a source of energy or degrade pesticides while using other energy sources such as organic carbon. The rates of microbial degradation will be highest under a favorable condition for soil biota such as a warm, moist and neutral pH environment (Gavrilescu, 2005).

Chemical degradation is an abiotic process, including hydrolysis, oxidation-reduction reactions, and ionization (Gavrilescu, 2005). Pesticides can be degraded by sunlight. Photodegradation occurs not only in the air but also in the shallow soil where photons can penetrate (Gavrilescu, 2005).

Mobility. Pesticides in soils exists both as particles and as solute and the phase influences their mobility and transport mechanisms. Table 3.3 summarises the key properties of pesticides and environmental conditions that affect the mobility.

Table 3.3: Key pesticide and environmental characteristics that control pesticide mobilisation (modified from Gavrilescu, 2005)

  mobilised as solutes mobilised as particles
Pesticide characteristics
organic carbon-water partitioning coefficient (Koc) low high
water solubility high low
persistent high high
Environmental characteristics
soil texture low high
soil pH low high
organic matter content low high

The organic carbon-water partitioning coefficient (Kow) of pesticide is the ratio of the concentration of a chemical compound in the n-octanol phase to its concentration in the aqueous phase at equilibrium in a two-phase octanol/water system. Kow is usually expressed as logKow, which is inversely related to water solubility. It is frequently used to predict the distribution of a substance in water and soil.

It is related to the soil adsorption coefficient (Kd) which is described as:

Kd = Concentration of compound in soil / Concentration of compound in water

Kd usually varies greatly because the organic content of is extremely variable also. Nevertheless, adsorption occurs predominantly into the organic matter of the soil, therefore it is more useful to express the distribution coefficient in Koc.

Koc is also known as organic carbon-water partition co-efficient and is described as:

Koc = (Kd * 100)/ % Organic carbon

Koc may be the most important property to determine whether a pesticide is transported as particles or as solutes (Gavrilescu, 2005). The range may not be precise though, in general, pesticides with high Koc values (>100) – the herbicides trifluralin, paraquat and glyphosate – are likely adsorbed onto soil particles and lost via erosion. Such pesticides may accumulate in the soil and degrade over time releasing daughter compounds. While pesticides with intermediate Koc values (0.1< K<100), which are most herbicides today, are primarily lost with water (Fawcett et al., 1994).

Water solubility may be another important property to determine how easily a pesticide can be transported in water. In general, a pesticide with water solubility of less than 1 ppm is likely to adsorb onto soil particles (Gavrilescu, 2005). If a pesticide is not persistent, because it is transformed into different forms, it will less likely be mobilised either by solutes or particles.

Soil texture, pH and organic contents may also be important environmental factors to determine the degree of adsorption of pesticides (Gavrilescu, 2005; Table 3.3). The soil texture controls the available surface areas where adsorption can occur. Soil pH affects the pesticide solubility and microbial degradation rates; consequently, the adsorption rate will change. In general, in acidic soil, a pesticide is more soluble and microbes degrade the pesticide faster. Organic matter in soil provides binding sites to pesticides and serves as energy source for microbial reactions/degradation.

Pesticide cycle in the agri-hydrogeochemical system

The persistence and mobility of pesticides are mainly determined in the root zone. Below the root zone, pesticides are mainly conservatively transported. Microbes play the dominant role in degrading and transforming pesticides (Fenner et al., 2013; Gavrilescu, 2005). Although some researchers reported that microbial degradation in groundwater is potentially possible (Janniche et al., 2012), due to scarcity of energy source and nutrients, the rates of microbial degradation below the soil layer is generally insignificant compared to that in the soil layer. Therefore, once pesticides leach out of the root zone, they are redistributed without any significant transformation or degradation (Figure 3.5).

After a pesticide is applied in an agricultural field, it can be released back to the air via evaporation and volatilisation. In addition, it can be degraded by light in the shallow soil layer and be emitted to the atmosphere (Figure 3.5). Some fractions of the pesticide may accumulate in crops. Depending on its property, the pesticide can be adsorbed onto the soil particles (PES in Figure 3.5) or dissolved in soil pore water (PEW; in Figure 3.5). MSoil microbes can degrade the pesticide, producing daughter compounds (PED; Figure 3.5).

Pathways of pesticides in the agri-hydrogeochemical system

A potential pathway of pesticides through the atmosphere is spray drift (Figure 3.5). When a pesticide is applied as spray, it can be drifted directly into the adjacent surface water especially under windy conditions (Carter, 2000).

Surface erosion is a pathway to transport pesticides that are adsorbed onto soil particles (Figure 3.5). The dissolved phase of pesticides and the daughter compounds are transported via water pathways: pesticides are transported vertically via matrix flow or/and preferential flow and laterally via overland flow, interflow, tile-drainage, and groundwater (Figure 3.5).

The application of pesticides for plant protection purposes on agricultural fields is a diffuse source (Carter, 2000). On the contrary, point sources are localised situations such as tank-filling respectively cleaning, farmyard-runoff and spills from agricultural sources, fruit washing facilities or even sewage plants (Carter, 2000). Point sources are mainly due to misuse or inadequate management. Especially after heavy precipitation events, farmyard runoffs together with field runoffs produce contamination peaks and account for most of the contaminant load of small streams. In a catchment area with individual agricultural farms scattered and no other possible contamination source present, farmyard runoff accounted for 89.8 % of pesticide contamination, especially fungicides and insecticides (Neumann et al., 2002).

Pesticides of diffuse pollution will be transported via the pathways mentioned above, depending on their properties and environmental conditions, but pesticide pollution from point sources are mostly directly transported into water (Carter, 2000). For instance, poor management of filling/cleaning facilities may result in discharge of pesticides along the impermeable surface or via pipes (Wenneker et al., 2010), acting like overland flow. Illegal discharge of pesticides will directly flow into stream or groundwater, bypassing all the pathways (Carter, 2000).

Issues following an inappropriate usage of pesticides and alternative entry paths into the environment are addressed in the EU guideline 2009/128/EG. The Member States are obliged to transform the guideline into National Action Plans, to introduce measures to protect aquatic environments and to organise the education of pesticide applicants in the correct handling, disposal and cleaning of pesticide application devices.

Challenges in pesticide monitoring and regulation

Up to present, the pesticides that are found in the different environmental compartments can only sporadically be related to application data of the pesticides, since a regional differentiated data compilation of application data and a consequential estimation of the pesticide inputs is missing (SRU, 2016).

According to the approval procedure and a proper usage of pesticide products, no pesticide transport to surface or groundwater and no accumulation in soil should take place. However, EU-wide, a number of pesticides are detected in surface- and ground waters, the most abundant are listed in Table 3.4.

Pesticide contamination in surface waters being reported by EAA (WISE-databank, reports by Member States) are in their large majority due to substances, which are withdrawn from the market some time ago. In most sites, this is due to occationally high quantities of pesticides contained in the water table that feeds surface water. In some sectors, also fraudulent use of pesticides had been proven (Laurent, 2015).

Table 3.4: Most abundant pesticides being detected in surface- and ground waters: number of waterbodies (WB) not achieving a good chemical status due to pesticides
and number of Member States (MS) affected (EEA, 2018a; University of Hertfordshire, 2017)

Substance CAS- or EEA-No. Chemical group Type Examples for product with AS Introduced to the market Current market situation No. of WB not achieving good chemical status No. of MS with WB not achieving good chemical states for the listed substance
Surface waters
Isoproturon 34123-59-6 Urea derivative Herbicide Arelon, Azur, Alpha IPU, Alpha Isoproturon, Koala, Trump, Javelin, Javelin Gold, Protugan, Tolugan Extra 1971 approved EU-wide since 2003, with exeption of CY, DK, EL, FI, MT; withdrawn in 2016 199 8
Hexachlorhexane 608-73-1 Halogenated hydrocarbon, organochlorine Insecticide, acaricide Lindan 1945 on the market since 1945; acording to EC 1107/2009 not approved 120 11
Trifluralin 1582-09-8 Dinitroanaline Herbicide Alpha Trifluralin 48EC, Ardent, Fargro Axit, Treflan, Uranus, Elancolan 1961 approval withdrawn in 2007 acording to EC 1107/2009 12 6
Chlorfenvinphos 470-90-6 Organophosphate Insecticide, acaricide, veterinary substance Vinylphate, Birlane, Steladone, Supona, Apachlor, Haptarax 1962 not approved 10 4
Atrazine 1912-24-9 Triazine Herbicide Gesapri, Fenamin, Atrazinax, Weedex, Primaze, Atratol, Radazine 1957 not approved 9 4
Simazine 122-34-9 Triazine Herbicide Sanazine, Simanex, Amizina, Eagrow, Derby 1960 not approved 5 2
Alachlor 15972-60-8 Chloroacetamide Herbicide Lasso, Alanex, Pillarzo 1936 not approved 5 3
Pentachlorphenol 87-86-5 Organochlorine Insecticide, Herbicide, fungicide, molluscicide, plant growth regulator, wood preservative   1936 not approved 3 3
Ground waters        
Pesticides EEA_34-01-5  Active substances in pesticides, including their relevant metabolites, degradation and reaction products           345 11
Bentazone  25057-89-0  Benzothiazinone  Herbicide  Basagran, Zone 48, Troy 480, Herbatox, Leader, Laddox  1972  approved in all EU-countries  31  5
Atrazine 1912-24-9  Triazine Herbicide Gesaprim, Fenamin, Atrazinax, Weedex, Primaze, Atratol, Radazine 1957  approval expired  60  8
Desethylatrazine 6190-65-4 Dealkylated atrazine metabolite Plant growth inhibitor       69 5
Desethylterbuthylazine 30125-63-4   metabolite       34 4
Terbuthylatrazine 5915-41-3 Triazine Herbicide, microbiocide, algicide Calaris, Skirmish, Gardo Gold 1967 expired in DK, EE, FI, FR, LT, LV, MT, SE 25 4
Desisopropylatrazine 1007-28-9   metabolite       16 4
Bromacil   Uracil Herbicide Hyvar X bromoacil, Borocil 1V, Cynogan, Borea, Krovar II, Urgan 1961 expired 13 5
Simazine 122-34-9 Triazine Herbicide Sanazine, Simanex, Amizina, Eagrow, Derby ca. 1960 expired, except for ES 17 5
Metholachlor 51218-45-2 Chloracetamide Herbicide Dual, Bicep, Pennant, Pimagram 1976 expired 58 3
Alachlor 15972-60-8 Chloracetamide Herbicide Lasso, Alanex, Pillarzo 1969 expired 63 1
Acetochlor 34256-82-1 Chloracetamide Herbicide Harness, Trophy, Trophee, Acenit, Guardian, Sacemid, Surpass 1985 expired 32 1
Dicamba 24-00-9 Benzoic acid Herbicide Di-Farmon R, Foundation, Prompt, Relay P, Banval ca. 1963 expired in MT and SE 22 2

Using the WISE-databank, the causes of pesticide contamination of ground waters cannot be identified precisely, because obviously some of the Member States reported under a collective term, other reported the analyses of certain active substances. Most of the pesticides being reported contamining groundwater are not any more approved by EFSA-authorities (Table 3.4).

Tauchnitz et al. (2017) investigated in the German Harz foreland pesticide concentration in soils and surface waters. In surface waters, Glyphosate, Bentazone, AMPA, Diflufenican, Tebuconazol and Terbutylazin were detected. There was no correlation between agricultural application and detection of pesticides, possibly due to pesticide use/leaching from residential areas. Agricultural activities were clearly the reason for accumulation of pesticides in soils underneath agricultural activities. Especially Glyphosate and MCPA were found in depths of nearly five meters, S-Metolachlor and Pendimethalin in around one meter depth.

Ulrich et al. (2018) report the accumulation of herbicides and their transformation products in small water bodies or catchment areas. The autumn sampling focused on the herbicides Metazachlor, Flufenacet and their transformation products – Oxalic acid and – Sulfonic acid as representatives for common pesticides in the study region.

5. Nitrate and pesticides in the waterworks system

Agriculture has a direct influence on the concentrations of nitrates and pesticides in the raw water used for drinking water production. However, the raw water, coming from either groundwater or/and surface water, might be treated at the waterworks before delivered as drinking water to the consumers. Therefore the specific water treatment procedure at the waterworks is important for the final concentration of nitrates and pesticides in drinking water.

In Europe, drinking water is produced with different degree of treatment (Van Der Hoek et al., 2014; amended, Table 3.5):

  1. Without treatment
  2. With conventional treatments such as aeration and sand filtration;
  3. With advanced treatments such as active carbon filtration, advanced oxidation process (e.g., UV/H2O2, ozonation), desalination; and
  4. With combination of conventional and advanced treatments
  5. Mixing/dilution
  6. Excluding of polluted wells

Because nitrate is highly soluble and pesticides are persistent, the conventional treatments cannot remove them. Nitrate in water is removed via ion exchange, reverse osmosis, electrodialysis, biological/chemical/catalytic denitrification and combination among them (Kapoor et al., 1997).

The ion exchange process is running NO3- containing raw water through exchange resins, which contain strong base anions such as Cl- and HNO3-; therefore, nitrate is replaced with these anions. The reverse osmosis process is filtering out ions by pushing water through a semipermeable membrane. The electrodialysis process is transferring ions in a diluted solution to a concentrated one through a membrane with a direct electric current. The biological, chemical, and catalytical denitrification processes are denitrifying nitrate by biological (e.g. microbes), chemical (e.g. Fe (II)), and catalytical (e.g. palladium-alumina; Pd-Al2O3) agents, respectively.

To remove pesticides in water, advanced oxidation processes, coagulation-flocculation-sedimentation, nanofiltration, and active carbon adsorption are used (Ormad et al., 2008). The advanced oxidation process is to break down pesticides into biodegradable compounds using strong oxidants such as chlorine or ozone. This process is often combined with biological treatments; therefore, it is also called as a ‘preoxidation process’. The coagulation-flocculation-sedimentation is a physical and chemical process to remove pesticides by forming larger particles so that they can easily be separated out. The nanofiltration is filtering out pesticides using a filter membrane that has extremely small pore sizes. Pesticides can be removed by adsorption on the active carbon. These techniques are often used in combination.

Table 3.5: Summary of drinking water treatament methods in Europe (modification of Table 1 in Van Der Hoek et al. 2014)

Water process method Groundwater Surface
No treatment - -
Conventional treatment Aeration and/or Rapid Sand Filtration (RSF) Coagulation, sedimentation, and filtration (CSF)
Advanced treatment Carbon filtration, advanced oxidation process, membrane desalination, ion exchage, reverse osmosis, electrodialysis, nitrification, coagulation-flocculation-sedimentation, nanofiltration Carbon filtration, advanced oxidation process, membrane desalination, ion exchage, reverse osmosis, electrodialysis, nitrification, coagulation-flocculation-sedimentation, nanofiltration
Conventional + advanced treatment Aeration and/or RSF + advanced treatment CSF + advanced treatment

Table 3.6: Cost of water treatments in France (Juery, 2012)

  Treatment Cost (€/m³)
code1 Water intake + disinfection 0.05
code2 Pretreatment + coagulation/flocculation + sedimentation + sand filtration + disinfection 0.13
code3 Pretreatment + coagulation/flocculation + sedimentation + preoxidation + sand filtration + O3/activated carbon filters treatment + disinfection 0.20
code4 Code 3 without preoxidation + biological denitrification 0.38
code5 Pretreatment + coagulation/flocculation + sedimentation + sand filtration + disinfection+microfitration+nanofitration 0.43
code6 Ultrafiltration + disinfection 0.50

The level and type of water treatment of each country may depend on the quality of raw water, its financial circumstances, and strategic political decisions (WHO Regional Office for Europe, 2002). In general, advanced treatments (e. g. nanofiltration, advance oxidation process) are more expensive than the conventional treatment techniques. An example of treatment cost is shown for France in Table 3.6 (Juery, 2012). Disinfection alone costs in average 0.05 (€/m³) wheras ultrafiltration + disinfection cost in average 0.50 (€/m³) (Table 3.6). The costs of water treatment in other countries will differ from those in France (Table 3.6), because of differences in local conditons. Table 3.7 shows main types of drinking water treatments used in some countries (not specific to the case study sites) that are part of FAIRWAY (WHO Regional Office for Europe, 2002).

Table 3.7: Main types of water treatment methods for drinking water production (modification of Table 4.5 in WHO Regional Office for Europe, 2002)

Country Groundwater/spring water Surface water
Denmark
  • Aeration and sand filtration
  • Not used for drinking water supply
France
  • Disinfection only
  • Some nitrate removal (ion exchange and biological denitrification)
  • Chemical coagulation, advanced oxidation process, disinfection
  • Few waterworks with membrane technology
  • Some nitrate removal (ion exchange)
Greece
  • Disinfection only, using chlorine
  • Aeration and sand filtration
  • Iron and manganese removal for some source
  • Some nitrate removal (ion exchange and biological denitrification)
  • GAC and PAC use
  • Mostly chemical coagulation and disinfection
  • Some slow sand filtration
  • Removal of pesticides by granulated active carbon or O3 with granulated active carbon
Germany (UBA 2016, 2018)
  • 70 % of drinking water derives from groundwater, another 7 % is artificially enriched groundwater
  • (Aeration), flocculation and sand filtration
  • In addition membrane processes and occasionally ocidation, ion exchange or activated carbon filtration
  • Occasionally nitrate removal by ion exchange, reverse osmosis, nanofiltration, biologic treatment or electrofiltration
  • 16 % of drinking water is surface water, further 8 % is water treated by bank filtration
  • In addition flocculation, filtration, O3 with granulated active carbon or disinfection
Netherlands
  • Aeration and multistage sand filtration
  • Extensive use of multistage treatment, including dune infiltration, coagulation, activated carbon, and disinfection with chlorine or O3
United Kingdom
  • Disinfection only, using chlorine
  • Iron and manganese removal for some source
  • Approximately 20 waterworks with nitrate removal (ion exchange)
  • Removal of organics (e.g., pesticides, solvents) by O3 and granulated active carbon
  • Mostly chemical coagulation and disinfection
  • Some slow sand filtration
  • Removal of pesticides by granulated active carbon or O3 with granulated active carbon

One of the challenges of drinking water production is disinfectant by-products (DBPs) such as thrihalomethanes (THMs) and haloacetic acids (HAAs; e.g. Van Der Hoek et al., 2014; WHO Regional Office for Europe, 2002). Disinfection processes are intented to eliminate pathogens in drinking water (Safe Drinking Water Committee, 1980) and it is particularly important for surface water. Van Der Hoek et al. (2014) reported that nearly 88 % of drinking water production – nearly 99.99 % from surface water and more than 70 % from groundwater – in Europe employes a disinfection process. Raw water is disinfected primarily via oxidation reactions using strong oxidants such as chlorine compounds (e. g. chlorine, hypochlorite, chlorine dioxide), ozone (O3), UV/H2O2 (World Health Organisation, 2000). Although it may be a minor risk in comparison to preventing waterborn diseases (WHO Regional Office for Europe, 2002), oxidation of some pesticides during the oxidation process can produce DBPs (e. g. Adams and Randtke, 1992; Chiron et al., 2000; Huang et al., 2009; Li et al., 2016). Nitrate may indirectly play a role in producing DBPs: excessive nitrate, along with phosphorus, in raw water (i. e. surface water) can trigger algae blooms. Then, while the algae cells are destroyed at the disinfection process, DBPs can be produced as well (e. g. Huang et al., 2009; Plummer and Edzwald, 2001).

In Denmark, groundwater protection has a high priority in order to secure clean drinking water for the population provided directly from the taps in the houses. Accordingly, the Danish groundwater protection policy is based on prevention rather that treatment at the waterworks.

Waterworks in Germany, according to a recently conducted nationwide survey, tend to avoid the implementation of expensive treatment measures in order to reduce high nitrate concentration in drinking water (Oelmann et al., 2017). Figure 3.7 shows clearly, that the so called “Preventive measures” (consulting farmers, cooperation between farmers and water works, including – financial – support, purchase or lease of land) are far more common than the so called “Reactive measures” (mixing, avoiding. e. g. excluding the polluted well, advanced treatment).

D3.1 fig03.7
Figure 3.7

 

 


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

» References

 

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. Nitrates 
2. Pesticides 
3. Indicator definition
4. The DPSIR framework

1. Nitrates

Nitrates are intermediate products in the nitrogen cycle (see »Nitrogen and pesticide cycles in the agri-hydro-geochemical system). As ions in soil water they are the most prevalant form of nitrogen being uptaken by plants.

Nitrogen statistics

Nitrogen containing fertilisers are the most used fertilisers in Europe (Eurostat, 2018). EU-wide, mineral nitrogen consumption as fertiliser in 2015 amounted to 11,362,000 tons, which equals an average of 75 kg N/ha utilised agricultural area (UAA) (Netherlands: 137 kg N/ha UAA: Romania: 28 kg N/ha UAA) (Eurostat, 2018). This figure does not include nitrogen from organic fertilisers, such as farmyard manure, compost, digestate or sewage sludge.

To total number of livestock in the EU amounted to 130,319,600 livestock units (LU) in 2013, which equals 73.8 LU/ha UAA (Netherlands: 359 LU/ha UAA; Bulgaria: 20.6 LU/ha UAA) (eurostat, 2018; Statistisches Bundesamt, 2015). Per rough estimation this corresponds to an extra N input to 7,300,000 tons or approximately 42 N/ha UAA.

The gross N budget amounted in 2015 to 51 kg N/ha UAA (Cyprus: 194 kg N/ha UAA ; Netherlands: 189 kg N/ha UAA; Romania: 9 kg N/ha UAA) (Eurostat, 2018). The result of the budget is always positive but varies largely between Menber States with an intensive animal and plant production and those, where extensive agriculture dominates. The budget surplus indicates nitrogen losses into air (as ammonia, nitrous oxide, nitrogen oxides and dinitrogen) and water (as nitrate).

The figures cited above show, on the European average, a nitrogen import on the field as mineral and organic fertilisers (including grazing) of 117 kg N/ha UAA and an export by crops of 66 kg N/ha.

Registration/placing on the market of fertilisers

Commercial mineral fertilisers, chelating agents, nitrification and urease inhibitors (and liming materials) are subject to the European fertiliser regulation 2003/2003. The regulation lists authorised types of EC fertilisers, including method of production, minimum concentration of plant nutrient and form and sulubilities of nutrients. Regulation 2003/2003 contains an open list of approved fertilisers, which is continuously amended, in order to add new fertiliser types, categories or improved analytical methods (EC, 2003). Amendments are effectuated upon application of a Member State and the fertiliser industry affected. There is no registration of organic fertilisers on the European level up to now, but the Comission plans a complex regulation system within the framework of “circular economy”. The European Parliament’s Internal Market Committee (IMCO) voted in July 2017 on amendments to the Fertiliser Regulation and suggested it be expanded in order to open the European market to more products such as organic fertilisers (Euroactiv, 2017).

On Member State level, there already exists legislation on the placement on the market of organic fertilisers (i.e. compost, digestate, manure).

Data requirements

The Nitrates Directive (91/676/EEG) was adopted in 1991 to protect waters against agriculturally derived N pollution. WFD (2000/60/EG) was passed in 2000 to protect European waters in order to reach “good status” objectives for water bodies throughout the EU.

Member States are required for the implementation of the Nitrates Directive to (i) establish monitoring networks in order to identify polluted or threatened waters; (ii) establish a voluntary code of good agricultural practice; (iii) allocate all land that drains into polluted waters as nitrate vulnerable zones (NVZ); (iv) establish mandatory action programmes within NVZ and (v) review the action programmes and NVZ boundaries every four years. In this connection, Member States have to report the quality of their surface and groundwater. Additionally, Member States have to report on their national action programmes. Impact assessment of the action programme measures may require Member States to provide information on the following elements:

  • Total number of farmers, and farmers with livestock, total land (km²)
  • Agricultural land (km²)
  • Agricultural land available for application of manure (km²)
  • Permanent pasture
  • Permanent crops
  • Annual contribution of mineral and organic forms of N (kg N/ha)
  • Annual use of mineral and organic N (kilotonnes)
  • Nitrogen discharge into the environment from agriculture, urban wastewater and industry (Oenema et al., 2011).

2. Pesticides

Pesticides are substances that are meant to control pests, including weeds. The term pesticide includes all of the following: herbicide, insecticides (which may include insect growth regulators, termiticides, etc.) nematicide, molluscicide, piscicide, avicide, rodenticide, bactericide, insect repellent, animal repellent, antimicrobial, fungicide, disinfectant (antimicrobial), and sanitizer (https://en.wikipedia.org/wiki/Pesticide).

Pesticide statistics

A regulation on the reporting duties of the Member States to the EU on statistical usage data of plant protection products (Regulation (EC) No 1185/2009) was published in 2009. This regulation contains details of the requirements in all Member States for pesticide statistics and reports on the progress of the implementation of the Framework Directive on the Sustainable Use of Pesticides (FDSUP). All Member States have to collect sales and usage data to provide insights into the amount of pesticides sold and applied per crop and area. Statistics regulation (EC) No 1185/2009 requires, that the nationally sold annual weight (kg) of all active substances listed in its Annex III are collected under certain major groups and product categories.

Required usage data for pesticides refer to representative crops (selected by Member State) within a one-year reference period and a 5-year reporting. Key pieces of data required are the quantity (kg) of each substance used on each crop, and the area (ha) treated with each substance. Usage data to be reported include pesticide consumption, pesticide characteristics, soil characteristics, application rates, application timings and mitigation measures.

D3.1 tab02
Table 2.1

Table 2.1 shows the pesticide sales per hectare (UAA minus permanent grassland) as total and split into the different types of pesticides, in kg of active ingredient per hectare, for each of the 28 European Member States and as European average. Pesticides are used in far smaller quantities than fertilisers: on the European average, pesticide sales amount to 3.18 kg/ha [UAA minus permanent grassland], of which fungicides and bactericides take the largest share with almost 1.39 kg/ha [UAA minus permanent grassland], herbicides and similar type of substances take a share of 1.05 kg/ha [UAA minus permanent grassland]. For this indicator, areas of permanent grassland are subtracted from UAA, as pesticides are not regularly applied on grassland, which is mostly used as feedstuff for animals. It is noticeable, that some Member States have reported sales of active substances far over the European average: these are Belgium, Cyprus, Italy, Malta, the Netherlands, Portugal, Slovenia and Spain (Eurostat 2018, 2018b). In most of these cases, the sales of fungicides and bactericides, herbicides and related products (active ingredients) are elevated in comparison to the European average.

However, the quantity of active substances sold does neither gives clear information on the toxicity of the pesticides used, nor on their persistence or on other chemical characteristics. Therefore, an interpretation towards the intensity of pesticide use is difficult and may include the following factors:

  • range of pesticides being approved for the zonal market of a Member state. This varies probably a lot and may depend on the market size: in small markets it may be less interesting for pesticide producers to register new products: there, more of the older products may still be in use. Those products are generally applied in higher concentracion per hectare as products being put on the market recently,
  • climatic distinctions,
  • cropping patterns/range of crops being cultivated,
  • intensity of crop production.

Registration/marketing licence for pesticides

A two-step approval procedure for pesticides is obligoratory in the EU. As first step, the placement of pesticides (=plant protection products) on the market is subject of the Regulation (EC) No 1107/2009. The central authorisation of the so called “active substances” of the pesticides is at the request of a Member State pronounced by European Food Safety Authority (EFSA) and approved by the European Commission. Application papers are confidential. Every active ingredient has to run through a complex admission system, before it can be developed as pesticide and provided to the user. An industrial company starts the process with the submission of an application of approval of a new active substance to an EU Member State. The application includes supporting scientific information and studies, including pesticide fate modelling for defined scenarios. The Member State evaluates the application. Subsequently, the European Food Safety Authority EFSA peer reviews the Member State's assessment of the active substance. On the basis of EFSA's review, the European Commission and the Members States decide whether to authorise the active substance. Active substances are approved for a period of 10 years. Industry has to apply for the renewal of the approval.

In a second step, a company applies to a Member State to put a pesticide containing an approved substance on the market. The Member State assesses the approval and puts forward a proposal for specific Maximum Residue Levels (MRL). If the proposed MRL is covered by existing legislation, the application is submitted to the EC. The EC decides whether to accept the proposed MRL; if it does, the Member State can authorise the pesticide for a defined usage zone (EFSA, 2018).

The pesticide then can be brought to the market. The Member States are required to monitor pesticide use and pesticide residues in food. The Framework Directive on the Sustainable Use of Pesticides (FDSUP) (2009/128/EC) contains requirements on training provision of pesticide advisors and spray operators, and the testing of spray equipment. This directive is implemented by the Member States in National Action Plans.

FOCUS, the FOrum for Co-ordination of pesticide fate models and their Use (EC, 2018b) runs a website, from which currently approved versions of simulation models and clearly definded scenarios can be obtained (for further detail, see »Agri-drinking water quality indicators at farm and drinking water levels). Both are used to calculate the concentrations of pesticides in ground- and surface water according to Regulation (EC) No 1107/2009. Furthermore, this website contains links to the reports of all FOCUS workgroups.

Standard scenarios were among other reasons introduced to faciliate a consistent scientific evaluation of the leaching potential of substances at the EU level. A Version Control Workgroup as a standing body ensures that the scenarios are updated in order to reflect scientific progress and representativeness for European conditions (EC, 2018b).

The EC website "Guidelines on Active Substances and Plant Protection Products" lists technical guidance documents under the topics physico-chemical analytical methods, efficacy, toxicity, residues, fate and behaviour and ecotoxicology EC, 2018c).

Under the topic “fate and behaviour”, an EC working document is published as Guidance Document on Persistence in Soil (DG AGRI, 2000). Under the same topic, a Guidande document on the assessment of the relevance of metabolites in Groundwater of substances regulated under the council Directive 91/414/EEC is published (DG SANCO, 2003).

Member States are also required to adopt – on a regional or national scale – harmonized risk indicators for pesticides, although these are still under development by the EU. Until then, the Member States may use national indicators (Oenema et al., 2011).

Evaluation of the EU approval procedure for pesticides

There has been criticism concerning the procedure for putting pesticides on the EU market. This refers to transparency aspects, but also to the systematical approach persued until present.

After discussions on the risk posed by the herbicide substance glyphosate and other pesticides, the EU Parliament decided in February 2018 to set up a special committee on the EU’s authorisation procedure for pesticides (PEST). Task of the special committee is to assess up to 12 December 2018

  • the authorisation procedure for pesticides in the EU;
  • potential failures in how substances are scientifically evaluated and approved;
  • the role of the Commission in renewing the glyphosate licence;
  • possible conflicts of interest in the approval procedure; and
  • the role of the EU agencies, and whether they are adequately staffed and financed to enable them to fulfil their obligations (EU parliament, 2018).

Element of the regular agenda is a REFIT evaluation of the EU pesticide legislation, in order to assess if the regulations meet the needs of citizens, businesses and public institutions in an efficient manner. The REFIT-evaluation is carried out by the Commission. The evaluation aims to perform an evidence-based assessment of the implementation of the regulations on pesticide and maximum residue levels and address synergies, gaps, inefficiencies and administrative burdens. According to the roadmap published by the Commission in November 2016, main evaluation criteria to be addressed in this REFIT evaluation are:

  • Effectiveness of the intervention;
  • Efficiency in relation to resources used;
  • Relevance in relation to identified needs and problems;
  • Coherence with other interventions with common objective;
  • EU added value compared to what could have been achieved by Member State or international action.

The whole process including stakeholder’s comments can be followed on the web page https://ec.europa.eu/food/consultations-and-feedback_en#fbk) (European Commission, 2018).

Adjustment needs for the EFSA evaluation procedure of the environmental impact of pesticide active substances

According to the EU guideline 2009/128/EG, pesticides should have, if used properly, no negative effects on the physical health neither of human beings or animals (with the exception of the target species) nor on surface and groundwater and the rest of the environment.

However, analyses by several research teams show that the current pesticide input has considerable negative effects on terrestrial and aquatic ecosytems and biodiversity (SRU, 2016). Several countries in Europe report that groundwater has concentrations of pesticides that exceed the quality standards. About 7% of the groundwater stations reported excessive levels for one or more Pesticide (Eurostat, 2018f).

Also surface waters showed abnormalities: nearly half of the insecticide concentrations in the European surface waters exceeded the regulatory accepted values (Stehle and Schulz, 2015).

Pesticide contamination is considered one of the reasons by which streams fail to achieve good ecological and chemical status, the main objectives of the Water Framework Directive. However, little is known on the interaction of different pesticide sources and landscape parameters and the resulting impairment of macroinvertebrate communities (Bunzel et al., 2014).

In aquatic systems, insecticides change structure (Liess and von der Ohe, 2005) biodiversity (Beketov et al., 2013) and function of aquatic biocoenoses (Schäfer et al., 2011, 2012). Worldwide, the size of populations of invertebratae has been reduced by around 45 % and the number of species sank drastically, too (Dirzo et al., 2014).

A meta-study by German, Danish and Australian universities in 2012 revealed that the current pesticide admission procedure is neither suited to meet the biodiversity targets for streaming waters nor the targets of the Water Framework Directive to establish a good ecological status of European water bodies. Their analysis showed that with concentrations that are not problematic according to the allowed standard procedures, the abundance of sensitive organisms was reduced by 27-61%, depending on how far unstressed upstream river conditions existed (Schäfer et al., 2012).

In terrestric systems, herbicides reduce diversity and abundance of flowering plants, especially of arable herbs. This results in a loss of feed for insects and a reduced diversity of insects, not only at the border of fields (Roß-Nickoll et al., 2004; Ottermanns et al., 2010; Legrand et al., 2011; Schmitz et al., 2014; Hahn et al., 2015) but in the whole agrarian landscape. Due to the massive reduction of biomass, structures of microhabitats and feed resources, not only insects but all consumers of insects, as small mammals and birds, are affected (= feed network; Hallmann et al., 2014; Goulson, 2015; Rundlof et al., 2015; Woodcock et al.; 2016; Hallmann et al., 2017; Vogel, 2017).

One reason identified is that the current admission procedures only assess the effect of single pesticides, a situation that does never occur in natural environments, where organisms are repeatedly exposed to multiple substances (Schäfer et al., 2012). Additionally, the presence of other active substances can reduce the degradation of a pesticide significantly, as was shown for the herbicide Pendimethalin, where the half-life doubled in the presence of Mancozeb (Swarcewicz and Gregorczyk, 2012).

The toxic effect of a pesticide mixture can, in comparison to the single substances, be enforced or reduced by mutual impact: the mixture can have additive, synergetic or antagonistic toxic effects as compared to the single pesticides. Moreover, the LD50/LC50 value for the standard reference organisms, used as toxicity indicator for terrestrial/aquatic organisms, does not allow conclusions on the effect of a pesticide on different species of an ecosystem, since the most sensitive species to a pesticide in an ecosystem is not known . Additionally, indirect effects such as secondary damages in the food chain are not accounted for by the LD50/LC50 values. Thus, the LC50 value of the single substances is not suitable as indicator for the impact of pesticides on an ecosystem, although often used that way (Fent, 2013).

Moreover, the standard admission procedures ignore the fact that organisms are exposed to multiple stresses in the environment which can increase their vulnerability against pesticides (Schäfer et al., 2012).

Another reason behind the adverse effects of pesticides on ecosystems are deficits in the pesticide prediction models concerning pesticide soil degradation and exposition of water bodies as well as in pesticide regulation. A Swiss monitoring-study revealed, that from a selected range of 80 pesticides applied to fields between 1995 and 2008, still 80 %, half of them metabolites, can be detected in small quantities in the soils (Bonmatin et al., 2015), although in the admission papers far shorter retention times are documented (Schäffer et al., 2018).

Risk assessments do not consider mixtures of active substances with each other or with fertilisers, sequential exposition and total load of pesticides (Schäffer et al., 2018).

Risk assessments during pesticide admission fall short of indirect effects such as loss of habitat and food resources following pesticide application. Risk assessments hardly consider multiple stress factors that add to the pesticide exposure, such as competition with less sensitive species, overfertilisation, narrowed crop rotations or consequences of climate change such as drought periods or extreme rainfall events. Many potentially affected species such as wild pollinators (bumblebees or wild bees) and amphibians are not integrated in the current risk assessments during pesticide admission tests.

The German monitoring of pesticide concentrations following the Water Framework Directive does not include all active substances relevant for the present agricultural practice and is therefore according to Schäffer et al. (2018) not suited to serve as a general representative monitoring for pesticides.

3. Indicator definition

Below, the relation between environmental, agri-environmental and agri-drinking water quality indicators (ADWIs), main subject of this report, is outlined.

Environmental Indicators

An environmental indicator is an index or a measurement endpoint used to evaluate the condition of a studied system. The term ‘‘indicator’’ is frequently used as a link between scientific results and policy making. Indicators are usually used to describe or extrapolate the future condition of habitats and to evaluate test whether a desired environmental condition is achieved.

Environmental indicators were developed by the Organisation for Economic Co-operation and Development (OECD) in the early 1990s. Main criteria for their selection were “policy relevance and utility for users”, “analytical soundness, and “measurability” (EAA 2014).

Indicators can be used for:

  • ex ante evaluations of actions during the planning phase,
  • ex post evaluation of actions at their end or implementation,
  • monitoring with an alert role,
  • decision support in real time to drive the system, and
  • communication (Bockstaller et al., 2008).

Diferent types of indicator can be distinguished (Bockstaller et al., 2008):

  • Simple indicators, based on one type of variable not directy measured, but obtained by surveys or databases. They can consist on one or a simple combination of variables and often show a poor quality of prediction.
  • Indicators based on conceptual or mechanistic simulation model allow to link the predicted effect to causes. Their complexity is a major limitation to use.
  • Indicators based on measurements. They are used when the focus lies on impacts and no accurate model is available. Disadvantageous are the costs.

The output of an indicator may be quantitative or qualitative, a reference value can assist in the interpretation of the individually calculated value (Bockstaller et al., 2008).

Lebacq et al. (2013) define a typology for four kinds of indicators Table 2.2):

  • means-based indicators, assessing technical means and inputs used on the farm, i. e. livestock stocking rate,
  • system-state indicators, concerning the state of the farming system, i. e. post-harvest soil nitrate,
  • emission-indicators related to the farm’s polluting potential, i. e. estimated farm’s loss of nitrates to ground- and surface waters and
  • effect-based – measured – indicators reflecting the impact of the practices on the environment, i.e. actual nitrate concentration in ground water.

While means-based indicators are easy to implement with regard to data availability and calculation, they show a low quality of prediction of environmental impacts (van der Werf et al., 2009). Effect-based indicators, on the other hand, directly reflect environmental impact, but are difficult to implement and data collection is often more expensive and time-consuming (Lebacq et al., 2013). System-state and emission indicators, ranging from budgets to complex model-based indicators, have an intermediate position.

Table 2.2: Description of the typology of environmental indicators and characterisation of these types, in terms of calculation method, data availability and environmental relevance, in the context of data-driven approach (Lebacq et al., 2013, adapted from Bockstaller et al., 2008; van der Werf and Petit, 2002; van der Werf et al., 2009)

Type   Example  Definition  Calculation  Spatial scale*)  Data availability*) Environmental relevance*)
Means-based indicators   Livestock stocking rate Agricultural practices Single variables  P/F  ++  -
Intermediate indicators System-state Amount of post-harvest soil nitrate State of the farming system Single variables, direct measurements P/F +/− +/−
  Emissions   Emissions of greenhouse- and acidifying gases, nutrients, pesticides into the environment and potential impacts           
  -Nutrient budget Farmgate nitrogen surplus Combination of variables F + +/-
  -LCA*) Eutrophication potential Emission factors F+ +/- +
  -Model-based Nitrogen leaching modeling Modeling P/F/R - +
Effect-based indicators   Nitrate concentration in groundwater Environmental impact Direct measurements W/R -- ++

*) LCA life cycle analysis; P parcel level; F farm level; F+ farm level, including upstream activities (e.g., production and transport of inputs); R regional level; W watershed level;
++, +, +/−,−, −− relative degree of data availability and environmental relevance***

Agri-Environmental Indictors (AEI)

Agri-Environment Indicators (AEI) for monitoring the integration of environmental concerns into the Common Agricultural Policy (CAP) were further developed in 2002 by the IRENA (Indicator Reporting on the Integration of Environmental Concerns into Agriculture Policy) operation. It is an indicator set used by DG Agri, DG Environment, Eurostat and Joint Research Centre, and the European Environment Agency.

IRENA was organised as a joint project of DG Agriculture and Rural Development, DG Environment, DG Joint Research Centre, Eurostat and the European Environment Agency (EEA). The purpose was to develop and compile for EU-15 the set of 35 indicators defined in COM final 0020/2000 and COM final 0144/2001 at the appropriate geographical levels and, as far as possible, on the basis of existing data sources. Using the DPSIR-model, agri-environmental relationships with respect to the topics water, land use and soil, climate change and air quality, biodiversity and landscape were developed and 28 AEI were defined for the monitoring of environmental concerns into the CAP. Several limitations remain for a number of indicators (eurostat 2018):

  • deficiencies in the data sets related to certain indicators, in terms of harmonisation (e. g. farm management), or geographical coverage (e. g. water quality),
  • data availability (e. g. genetic diversity or pesticide risk),
  • requirement of further conceptual improvement (e. g. high nature value farmland areas).

DireDate, a project finalised in 2011 on behalf of eurostat, was run with the objective to set up a sustainable system for the collection of data sets from farms and other sources that would serve primarily European and national statisticians to calculate the 28 AEIs. The objectives of DireDate were to analyse and describe AEI data requirements, to provide recommendations for priority data collection and to analyse the feasibility for a combined data collection and processing. Methodologies for the calculation of combined indicators, i. e. the farm nutrient budget, were presented.

Certain types of data can be obtained from the Farm Structure Survey (FSS) and from the Survey on Agricultural Production Methods (SAPM), however, also individual farm data on animal feeding, animal housing, manure storage and manure application are needed for the calculation of farm nutrient budgets. Oenema (2011) pointed out, that the EU Member State systems for collection, processing and reporting of agri environmental data need increased coordination, harmonisation and streamlining throughout the whole chain.

The level, on which the AEI are used and the purpose they may be used for on these levels differs with scale:

  • European/national level: The application of AEI enables e. g. the European Comission to evaluate/benchmark the transcript of EU-legislation at Member State level. Under the topic “Agriculture and environment (AEI),” 13 AEI are listed for the Member States, partly on NUTS 2 regional level (Eurostat, 2018c). At the national level, AEI are typically based on/calculated from existing statistical data, as it is not possible to either find detailed data or it is too expensive to start collecting them for a whole country (Niemeijer and de Groot, 2008).
  • Regional level: AEI are used e: g. on supranational/regional/local context, to monitor the impact of agriculture on environment, identify hotspots or focus subjects and areas for the agricultural advisory service.
  • Farm level: On farm level, the nitrogen farm budget as AEI could be used, first of all, as decision aid tool, to help farmers to adapt their cultivation practices to integrated arable farming system requirements, from one cropping year to the next (Bockstaller and Girardin, 1997). This is the case e. g. in Denmark, France, Germany, the Netherlands, Portugan and Romania. On this level, AEI are used for benchmark-purposes, too, i.e. to compare the management of the same type of farms and to focus on “low performers”. Besides the calculated budget-indicators, measured indicators play a larger role for practical farm consulting. For example, the harvest Nmin-concentration of arable soils is a meaningful indicator for nitrate leachate in winter and contamination of ground water (Osterburg and Runge, 2007).

 

D3.1 fig02.1
Figure 2.1

Figure 2.1 visualises the levels of operation of AEI in relation to the aim for their use and examples for corresponding indicators. The figure shows, that the degree of data aggregation increases with level of operation. In the other direction, the degree/proportion of individual farm data and measurements increases from European towards farm level.

As more (in time and space) aggregated data show less standard deviation than the single datasets, correlation with water quality could be stronger between AEI being deduced from data on a regional level than on farm level. This would explain, why Wick et al. (2012) found the Gross Nitrogen Budget a statistically significant predictor for groundwater nitrate concentration, while other authors (Buczko et al., 2010; Lord and Antony, 2002; Rankinen et al., 2007; Sieling and Kage, 2007) calculated less strong relationships for indicators at a smaller scale.

From the above in can be concluded, that on the different levels of operation, AEI may be the same, or they may differ in the parameters included.

4. The DPSIR framework

The DPSIR model is defined as “causal framework for the description of interactions between society and the environment”. Based on the PSR (pressure – state – response) model developed by OECD, it was adopted by the European Environment Agency (EEA, 2018). According to its terminology, social and economic developments (driving forces, D), exert pressures (P) on the environment and, as a consequence, the state (S) of the environment changes. This leads to impacts (I) on ecosystems, human health and society, which may elicit a societal response (R) that feeds back on driving forces, on state or on impacts via various mitigation, adaptation or curative actions (Smeets and Weterings, 1999; Gabrielsen and Bosch, 2003).

The DPSIR-model in the environmental context

In the agri-environmental context, the indicators of the DPSIR-model can be interpreted as follows (Gabrielsen and Bosch, 2003):

  • Driving forces describe the social, demographic and economic developments in societies and the corresponding changes in lifestyles, overall levels of consumption and production patterns, such as the preference for meat in diets.
  • Pressure indicators describe developments in emissions, the release of physical and biological agents and the use of resources including land by human activities. As result, a variety of natural processes lead to changes in environmental conditions, i.e. in an increase in ammonia emissions or in nitrogen deposition in natural habitats.
  • State indicators give a description of the quantity and quality of physical, biological and chemical phenomena, such as the concentration of nitrates in surface- and groundwaters.
  • Impact indicators show the impacts on the functions of the environment, such as human health and quality of ecosystem, resources availability, losses of manufactured capital, and biodiversity.
  • Response indicators refer to responses by society, as well as government attempts to prevent, compensate, ameliorate or adapt to changes in the state of the environment. The reduction of meat consumption as societal response can be regarded as negative driving force, since prevailing trends in consumption and production patterns are redirecting. Other responses may be to increase the efficiency of agricultural production, i.e. nitrogen efficiency in plant production.

Annex 1 in »FAIRWAY Project Deliverable 3.1 lists the 28 European AEI and shows how they are embedded in the DPSIR framework (Eurostat, 2018).

Application of the DPSIR model in different contexts and levels

The DPSIR model is used on different contexts and scales.

  • European/national level: The data on national level behind each of the 28 AEI are listed in fact sheets related to COM final 0508/2006 (Eurostat, 2018). On the European level and in relation to water quality, there are quite a few approved AEI which work as driving forces, but only some AEI function as pressure and risk indicators with focus on water quality: Nitrate pollution and Pesticide pollution (Annex 1 in »FAIRWAY Project Deliverable 3.1 and Figure 2.2). While the indicator “Gross nitrogen budget” is well defined, although further implementation might be necessary, the indicator “Pesticide risk” needs further development: The conceptual and, where appropriate, modelling framework underpinning this indicator needs to be developed (COM, 2016; Eurostat, 2018).
  • Regional level: Breaking down the regulations of the WFD on the level of river basin management/ground water bodies, the DPSIR-model can be applied to explain the mechanisms of the transformation of the Directive on this regional level. Certain targets, like water quality indicators, have to be met at this level. These AEI are also used for monitoring and control purposes.
  • Farm level: The DPSIR-model can also be applied on farm level. The compliance with national fertilising legislation, for example in Germany, has to be proven by setting up a net nitrogen soil (surface) budget; since the beginning of 2018, for intensive animal breeding farms, a gross nitrogen farmgate budget is compulsory, too. The result of these budgets, on farm level, serve as proof of “good agricultural practice”, the compliance with the rules of the nitrates directive and the fertilising legislation, also in the framework of cross compliance.

D3.1 fig02.2
Figure 2.2

 

 


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

» References

 

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. Indicators at farm level in the DPSLIR framework 
2. Indicators at drinking water level in the DPSLIR framework
3. Indicators for linking farm and drinking water levels in the DPSLIR framework

In Annex 1 in »FAIRWAY Project Deliverable 3.1, all 28 AEI according to COM final 0508/2006 and applied on EU-level are listed (COM 2006, eurostat 2018). The AEI are allocated to domains and subdomains of the DPSIR framework.

In the table below we have compiled all agri-drinking water quality indicators (ADWIs), which were reported to us during a survey among the in the FAIRWAY case studies and we supplemented the table with indicators according to a literature review. They are grouped according to whether they are relevant at farm (driving force and pressure indicators) or drinking water level (state/impact indicators) or if they provide a link between the two.

1. Indicators at farm level in the DPSLIR framework

Domain Sub-domain Indicator category Indicator
Impact Societal and economic demands  
  • Demands for clean drinking water *)
  • Population density *)
  • Cost for drinking water production *)

*) Indicator not discussed in this report

Driving forces               Resource management and planning   Land use planning
 
Agricultural preconditions
Farm management          Farming standards
Farming intensity
Farm management
N-fertilisation
Pesticide application
Trends  
Pressure  Leaching Leaching travel time
    Leaching quantity
    Nitrogen in soil water
     
  Surface water pollution  
    Pesticides in surface water
  Point sources  
  Aerial emission  
  Nitrogen efficiency Nitrogen budgets

 

2. Indicators at drinking water level in the DPSLIR framework

Domain Sub-domain Indicator category Indicator
State/Impact Water quality  
  Regulatory compliances  

 

3. Indicators for linking farm and drinking water levels in the DPSLIR framework

Domain Sub-domain Indicator category Indicator
Link Catchment typology   
  Lag time  
  Source identification  
  Vulnerability of the hydrogeologic system Nitrate vulnerability assessment
    Pesticide vulnerability assessment
  Environmental risk Nitrogen loss indicators - overview Nitrogen loss
    Pesticide risk indicators - overview

 


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

» References

 

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. Definition of agri-drinking water quality indicators (ADWIs) 
2. DPS(L)IR framework

1. Definition of agri-drinking water quality indicators (ADWIs)

One of FAIRWAY's tasks is to develop (i.e. to prioritise and evaluate) data-driven indicators which can be applied to detect, monitor or even predict the pollution of ground- and surface water by nitrates and pesticides.

Agri-environmental indicators (AEIs), as developed by OECD and Eurostat, are implemented and further developed for the monitoring and evaluation of the impacts of agricultural activities on the environment. These impacts may be negative and positive, on and off the farm. Negative impacts include pollution and degradation of soil, water and air, while positive effects include ecosystem services, such as mitigation of flood risks through the adoption of certain farming practices (OECD, 2018).

Consequently, agri-drinking water quality indicators (ADWIs) to be developed in FAIRWAY may be defined as indicators for the quality of drinking water. As drinking water may be produced from ground- or surface water, ADWIs aim at the quality of both. ADWIs may be identical to AEIs, or they may be different.

2. DPS(L)IR framework

According to the FAIRWAY's objectives, ADWI shall be defined within the DPSIR-framework. Having in mind, that ADWIs may be construed as a share of AEIs, there is not much difference between the AEIs interpretation within the DPSIR-framework and the interpretation of ADWI within the DPSLIR-framework. The adjusted DPSLIR-framework contains a new element, the Link indicators, which will be further explained in »Agri-drinking water quality indicators at farm and drinking water levels (Table 4.1).

Table 4.1: Interpretations of the DPS(L)IR framework for AEI and ADWI

Domain Description*) AEI interpretation**) ADWI interpretation
Driving force “Social, demographic, and economic developments in societies and the corresponding changes in the lifestyle and overall levels of consumption and production patterns” * “the state and evolution of regional farming system in relation to input use, land use, and management practices” Social, demographic, and economic demands for clean drinking water and the corresponding changes of the agricultural system in relation to input use, land use, and management practices
Pressure “Developments in release of substances (emissions), physical and biological agents, the use of resources and land“ “harmful and beneficial processes attribute to agriculture” Inputs of nitrate and pesticides from the agricultural system to the hydrogeological system
State “Quantity and quality of physical phenomena, biological phenomena, and chemical phenomena“*  “the state of different natural and semi-natural resources in rural area” Quality of drinking water resources
Link Natural and anthropogenic processes of transport and evolution of nitrate and pesticides in natural systems (from farm fields to water abstraction points) - Natural and anthropogenic processes of transport and evolution of nitrate and pesticides in the hydrogeochemical system
Impact “Relevance of changes in the state of environment“* “the share of agriculture, as a sector, to undesirable changes in the state of the environment resources and its effective contribution to the preservation/enhancement of other environmental resources” Public health concerns and regulatory compliances
Response “Groups and individuals in society and government attempt to prevent, compensate, ameliorate, or adapt to changes in the state of environment“* “Societal, market, and policy responses that influence production systems and agriculture practices” Implementation of mitigation measures

*(Stanners et al., 2007); **(EEA, 2005)

 

 


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

» References

 

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:

Contextualization

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).

Feasibility

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      
Legislation      
Precipitation/evapotranspiration 2 2 12
Temperature      
Wind      
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

 

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