|Main authors:||Berit Hasler, Ingrid Nesheim, Morten Graversgaard, Susanne Klages, Doan Nainggolan, Claudia Heidecke, Luke Farrow, Isobel Wright, Gerard Velthof, Sandra Boekhold|
|Source document:||»Hasler, B. et al. (2021) Identification of cost-effective and coherent management models for drinking water protection in agriculture. FAIRWAY Project Deliverable 6.4R 55 pp|
In »Farming practices: review and assessent, we present an extensive literature analyses of meta-analysis of effects and costs of nitrogen and pesticide abatement measures. In »Governance arrangements in case studies the FAIRWAY survey data from case studies are used to describe the coherence and consistency of EU Directives and policies and how they apply to farm water management, with specific reference to diffuse pollution of nitrates and pesticides. These analyses and results are used and further developed in this section of FAIRWAYiS.
|1. Best management practices and measures from case studies|
|2. Cost-effectiveness studies|
|3. Policy instruments|
|4. Legal instruments|
To assess the best management practices and measures from case studies, we use inputs from
- »Management practices that reduce nitrate transport, »Management practices that reduce pesticide transport, »Governance arrangements in case studies and additional literature to explore the costs and effects of assessed measures, and
- from »Governance arrangements in case studies we use the survey results to inform what barriers are found to implementation on in the 13 case studies.
- From »Management practices that reduce nitrate transport, »Management practices that reduce pesticide transport we reproduce the overview of measures assessed in the case studies (Table 2.1). This overview presents both pollution control effectiveness and costs, as perceived by the experts answering the case study surveys. Is is apparent from the table that even though this survey should have covered all 13 case studies, answers are only available from few countries per measure/practice – from one country (e.g. tax) to three (e.g. obligatory reduced input).
Table 2.1: Pesticide measures studied within the FAIRWAY case studies.
Effectiveness and costs are with indicated properties based on expert judgement by experts working in the case study.
|Safe pesticide cleaning and storage facilities||NL, UK-NIR||Neutral||Moderate Positive||No data|
|Safe storage unit for pesticides||UK-NIR||No data||Moderate Positive||No data|
|Vegetated filter strips||FR, SL||No data||Very positive||Moderate|
|Crop rotation improvement||FR||Moderate positive||No data||High|
|Input reduction||FR, UK-EN||Moderate positive||Positive||High|
|Network engagement||UK-EN||No data||Positive||No data|
|Alternative (pesticide or mechanical)||UK-EN, UK-NIR||No data||Positive||No data|
|Integrated Pest Management||UK-EN, DK||Very positive||Positive||Moderate|
|Obligatory reduced input||PT, DK, SL||Very positive||Very positive||Low|
|Bio filters/beds||UK-NIR||No data||Moderate positive||No data|
|Economic/Tax management||DK||Very positive||No data||Moderate|
|Drift reduction||NL||No data||Moderate positive||Low|
*UK-NIR is Northern Ireland, UK-EN is England
Safe storage of pesticides is reported by some countries, but the requirement exists in many countries. No data exist on the costs however. Filter strips are indicated as an effective measure to surface water, and it can be added that the literature finds this measure to be relatively costly (Gooday et al 2014, Konrad et al 2014, Hasler et al 2019). But this measure also have a positive effect on nitrogen and phosphorus losses, for phosphorus tree planting in the buffer zone improves the effect even more.
Pesticide measures include on site measures to reduce pesticide transport from the field to water bodies. The information of the measures was retrieved from literature, as well as from the case studies. Important differences exist between the countries in FAIRWAY. In some countries leaching from the field is the main threat, such as in Denmark, Netherlands and Germany. In both the literature review and case studies it is concluded that no measures exist to reduce the leaching. We would add to this that an exception is measures that reduce input/application. Taxes or quotas are instruments that aim to regulate inputs, but there are not many examples of tax or quotas on pesticides in practice. One example exist, as a tax instrument on pesticide has been implemented in Denmark. The tax was first implemented as ad valorem tax in 1996, meaning that the tax was on the pesticide price. The tax was doubled in 1998. Evaluations showed low effects, and the tax was redesigned in 2013 as a tax based on the toxicity of the pesticides (Pedersen et al 2015). Pedersen et al. evaluated the old and new pesticide tax, and conclude that the Danish pesticide taxes probably represent the world’s highest pesticide tax on agriculture, and that experience show that it is challenging to design an optimal tax level and design. One highlighted factor in Pedersen et als study is that barriers exist since farmers’ have different rationales when deciding on the pesticide application, and that not all are economically motivated. The conclusion is also that the new tax on toxicity seems to work better than the former but still the effect is small. Pedersen et al. conclude that one reason “for the small effects might be that about one third of Danish farmers can be considered to be less responsive to economic policy instruments than the main share of farmers, as the former focus more on optimizing yield than on prices on pesticides and crops.“ (Pedersen et al 2015, page 11). This is then a barrier for the tax to work efficiently and a quota might work better.
In Denmark there are also examples of other types of methods to reduce pesticide application, apparent from Table 2.1. Obligatory reduced pesticide input is attributed to zones close to groundwater wells, and the regulation is managed by the water works and municipalities. The implementation is relatively easy, but a lot of negotiation takes place, and the barrier in this type of regulation is the level of compensation. Voluntary reductions of inputs in these zones, and compensation, also exist. Reallocation of fields is also an instrument used in both Denmark and UK. In Denmark this instrument has proven to be efficient but just as long as the farmer voluntarily can see a benefit in the reallocation. The reallocation implies that the fields close to the well can be taken out of rotation, and the farmer is compensated by having another field.
Table 2.2: Nitrogen measure types applied and studied within the FAIRWAY case studies, with indications on effectivity, cost, applicability and adoptability.
|Measure type||Country||Target1||Pollution control effectivity2||Cost|
|Changes in cropping system or crop rotation||NL, SI||GW/SW/NUE||Positive||Low|
|Changes in fertilization timing||NL, DK, GR, RO, SI||GW/SW||Very positive||Low|
|Changes in application method||DE, DK||GW||Positive||Low|
|Changes in application dose (reduced input, balanced fertilization, or optimal fertilization)||NO, PT, DE, DK, GR, SI||GW/SW/NUE||Positive||Low|
|Cover crops||DK, GR, RO, SI||GW/SW||Very positive||Moderate|
|Buffer strips (either between crops and waterways, or between rows of crops)||NL, FR, GR, RO, SI||GW/SW||Positive||Moderate|
|Grassed waterways||NO||SW||Very positive||High|
|Farm-scale nutrient management tools||DE||NUE||Variable||Low|
|Outreach and information events||DE||NUE||Variable||Low|
|Other||GR||GW/SW||No data||No data|
1Target of the measure: GW groundwater; SW surface water; NUE nitrogen use efficiency
2Effectivity is evaluated as Low (+, 5-10% load reduction), Moderate (++, 10-25% load reduction), High (+++, >25% load reduction), Variable (*), or Unknown (?).
As can be seen, measures such as nutrient management tools are characterised as low cost measures, which is probably right, but this assumptions rests on that no changes will take place as part of using the tools. Crop rotation changes are also indicated as low costs, but that depends on the change. If a change in choice of crops is necessary it most often comes to a cost.
Another measure which is not mentioned here is the tradeable nitrogen quota in the Netherlands. This measure aims to control production of manure, was introduced for pigs in 1998, and poultry in 2001 with the trade restricted between regions. The quota has mainly reduced the excretion of phosphorus, by 15% between 2000 and 2003. The barrier for continued implementation is that it is an expensive measure and it will be changed.
In »Decision support tools, catchment level decision support tools for cost-effective nutrient and pesticide measures were reviewed, and the models TargetEconN and Farmscoper were presented in depth. The results from applying these two model concepts are relevant for illustrating the cost-effectiveness of measures to reduce nutrient and pesticide loads to water bodies.
The TargetEconN model developed and used in Denmark for water management at catchment scale. This model concept has been used to estimate the cost-effectiveness of measures for nutrient abatement to water bodies. Konrad et al. (2014) used TargetEconN in a catchment at Funen in Denmark, and the model is used to estimate the least cost spatial combination of measures to achieve nitrogen load reductions as required by the WFD in this catchment. They included 5 measures, and catch crops and wetland restoration where among the most cost effective measures (10.42 EUR/kg N for wetlands, and 12.17 EUR/kg N for catch crops). They also found that targeting measures to achieve least cost solutions taking the heterogeneity of both costs and effects into account and the variations in them due to soil types, land-use and retention of nitrogen in the catchment (hydrological attenuation) would reduce costs by approximately 30% compared to a uniform policy.
This finding is supported by Hasler et al. (2019) in a study of another catchment in Denmark, Limfjorden, also using the TargetEconN model. In a background report for this paper (Hasler et al 2015) the cost-minimization model was used to model different target levels for nitrogen load reductions in the Limfjord catchment in Denmark, and catch crops were the first chosen measure and most cost-effective measure at all target levels, being used up to maximum capacity level. The TargetEconN model is now set up for the entire country of Denmark, subdivided into 88 catchments, but results from this modelling is not yet available. The modelling is used to support the WFD implementation in Denmark.
Baltic Sea level cost effectiveness analyses apply the same type of concept as TargetEconN.Applications are described in Gren et al. (2008), Nainggolan et al. (2018) and Hasler et al, (2014). Catch crops are included as a measure in all of these model applications. Gren et al. (2008) find that for all the countries around the Baltic (Denmark, Estonia, Finland, Germany, Latvia, Lithuania) except for Sweden, restored wetland is the most cost-effective measure (lowest marginal costs in all countries to achieve the given HELCOM load reduction targets), followed by catch crops. For Sweden catch crops were most cost-effective, followed by restored wetlands. Hasler et al. (2014) and Nainggolan et al. (2018) minimized the costs of achieving both nitrogen and phosphorus losses to the sea basins, but did not assume any effect on phosphorus loads from catch crops. Therefore catch crops were not cost-effective in these model applications, because the phosphorus load reduction targets are required in most sea basins of the Baltic Sea.
Lacroix et al. (2005) also simulated nitrate pollution reduction scenarios including catch crops, and the study concluded catch crops are more cost-effective than 20% reductions of nitrogen inputs. They recommend that more attention should be made to the date of introduction of catch crops than to the reduction of inputs. The study argued that it is easier to control the date of sowing than to control reduction of inputs. The latter will reduce the administrative costs.
The Farmscoper model, developed and used for farms and catchments in UK, provides an assessment of diffuse agricultural pollution loads and includes both pesticides and nutrient pollution at farm – and catchment scale. The model has been applied as a policy tool in a number of studies to date (e.g. Micha et al., 2018; Collins et al., 2016; Gooday et al., 2014; Zhang et al., 2012). Over 100 abatement measures, typical for English and Welsh farms, are available for selection and their performance can be assessed based on the specifications of the farm and farm management input as a baseline. Both economic and environmental effects are assessed simultaneously and outputs are provided in tabular and graphical formats.. By applying an Upscale tool the model concept is suited to model WFD target achievement, as this upscaling prepopulate the model with catchment level census data for WFD waterbodies up to river basin scales (in England only).
The measures in the model are based primarily on the Defra Mitigation Method Guide and includes those relating to Cross Compliance, Catchment Sensitive Farming and the Countryside Stewardship Schemes in England and Wales. Each option has a full cost and effect estimate associated with it, and they are classified depending on whether their impacts relate to nutrients, livestock, soil, delivery or pesticides either singly or in combination. Measures applicable to arable crops, for example, include cultivation of compacted soils, establishing buffer strips, management of field corners, wild bird cover, uncropped margins and leaving residual levels of non-aggressive weeds in crops. For dairy farms, measures include increased scraping frequency in dairy cow cubicle housing and washing down of dairy cow collecting yards.
The initial scenario evaluated considers the impact of introducing mitigation methods that correspond to Cross Compliance Good Agricultural and Environmental Conditions, namely:
- 1 (Establishment of Buffer Strips along Water Courses),
- 4 (Providing Minimum Soil Cover) and
- 5 (Minimising Soil Erosion).
A 100% implementation of each measure on the farm was assumed, but it is possible to also consider a partial implementation (such as where a crop rotation is implemented and a measure is only active on a sub-set of fields in each year).
Several published studies have used Farmscoper at catchment scales to estimate the impact of a range of mitigation measures. Collins et al. (2016), for example, surveyed farmers across England to identify the mitigation measures more likely to be adopted by farmers and then applied the model to identify the potential reductions in emissions to air and water relative to business as usual. Business as usual emissions and uncertainties were based on comparisons with available monitoring data for England and Wales but acknowledging the limitations of available data in terms of low sampling frequencies and difficulties in disaggregating non agricultural sources.
Gooday et al 2014 appiled the model to dairy and cereal farm systems, and calculated cost-effectiveness ratios for a number of measures: buffer strips, cover crops and combined measures. They conclude that for dairy the cost-effectsiveness ratio was £5 per kg N both for cover crops and combined measures, while at £69 per kg N the cost for buffer strips was much higher.
For cereal farm systems the pattern was different: the costs per kg N for cover crops were still the lowest ( %5 per kg N), while the costs were £14 per kg N for mixed and £84 per kg N for buffer strips in cereals.
This short review based on spatial cost-effectiveness analyses modelling indicates that catch crops are cost-effective relative to other relevant nitrogen load reduction measures; some studies rate catch crops number second after wetlands. But the conclusion is that from a social planning point of view, catch crops have a large potential as a measure in cost-effective water management. Most of these modelling studies however, do not take farm and farmers heterogenity into account, nor uncertaintty or stocastic events into account.
To capture that kind of information it is necessary to conduct studies on farmers’ preferences, decisions and willingness to accept and implement the regulation in place. Examples of such studies are presented and discussed in »Detailed analysis of catch crops.
Policy instruments to motivate environmental practice in agriculture can broadly be categorized into economic incentives, legal mechanisms (also called command and control mechanisms), information and education incentives (Shortle and Abler 2001). Cross-compliance schemes and greening requirements can be defined as hybrids combining legal requirements with economic incentives.
Economic incentives refer to subsidies for environmental practice, comprising EU agro-environmental schemes (CAP Pillar II), but also to other subsidies on national and regional level. Economic incentives also refer to taxes and tradeable quotas on polluting inputs. These incentives are not mandatory, the farmer decides whether to implement the policy or not, according to his/hers judgement of the marginal costs. Cross compliance mechanisms, a combination of economic incentive and legal mechanisms imply that farmers’ eligibility for payments under a government agricultural program is conditional on specified agricultural practices (Shortle and Abler 2001).
Legal mechanisms generally define standards of practice under threat of penalty and sanction (e.g. required maximum livestock density as part of the Nitrate Directive), and states what is permitted and what is illegal.
Information and education type mechanisms aim to convince actors to carry out actions voluntarily. Typically, these instruments are designed to inform and support the implementation of other associated instruments.
The reviewed studies in »Detailed analysis of catch crops are built on the assumption that the governments are “social planners”. A social planner approach can be used to identify the most cost-effective combination of measures as well as spatial location of them without considering how to implement the changes. For the changes to take place in real life incentives have to be in place in order to encourage or force the actions. Governmental bodies therefore have to identify cost effective instruments to promote best management practices (Liu et al. 2018). Cost effective policy instruments can be defined as policies that fulfill the environmental targets to the lowest costs, but also with as low transaction costs as possible and with a high policy implementation.
Greening and Ecological Focus Areas
One option that applies to all EU countries is the Ecological focus areas (EFA) of Pillar I in CAP, where catch crops can be used for implementing the requirement (Schakelford et al 2019). The greening requirement and the environmental effect of this measure is important as it takes up 30 % of the total direct payment funds of the CAP. For farmers it is important that if they do not comply with the greening requirements they are penalized by a reduction or a removal of the greening part of the direct payments, and administrative penalties can also be added (European Commission, 2015, 2016).
The EFA part of the greening requirement imply that farms larger than 15 hectares should allocate at least 5-7% of their arable farm area to EFA. An adjustment factor of 0.3 is added to the implementation of catch crops, meaning that farmers have to cultivate more than three times of the utilized agricultural area (UAA) with catch crops in order to fulfil the set target (i.e. 16.66 % of the arable land in case of 5 % EFA obligation). In arable cropping regions, a share of 17 % summer crops is easily feasible (Hart et al., 2017).
Hart et al. (2017) made an evaluation on the CAP greening requirement focusing on the drivers of the choices taken by Member States and by farmers to fulfill the greening requirements. The greening requirements include catch crops, but also other measures, and as seen in Figure 1 not all European countries use catch crops as a greening measure. The results from this study is therefore not only attributed to catch crops, but also to the other types of greening measures.
Hart et al. also analyzed the effects of the measures on farming practices and production, effectiveness (in relation to their environmental and climate objectives); efficiency, coherence, relevance and EU added value. Hart et al. evaluation study report concluded, that for the farmers, the key factors driving decisions on how to implement the greening measures were:
- minimizing the risk of non-compliance and penalties while avoiding administrative complexity and burden;
- the degree of fit with existing farm practices, other CAP instruments (e.g. coupled support) and the requirements of cross-compliance and other legislation, such as the Nitrates Directive, to minimize any changes in practices or additional costs.
- Farmers’ choices may also have been affected by the extent to which information, training and support was available. Even though information and support services to farmers were in most cases judged satisfactory, a lack of coverage of environmental matters was reported in almost all Member States, with the focus of the advice available mainly covering administrative issues.
Hart et al. report that from responses to the public consultation conducted by the European Commission on the first period of greening, most farmers regarded greening as either difficult or very difficult to implement, and the reasons were economic, administrative or technical.
Fallow land and catch crops accounted for 55 % of the total EFA area (before weighting factors) in the EU in 2016, and large differences are found in the implementation of catch crops as part of the EFA requirement.
Schulz et al (2014) made a hypothetical experiment, applying the stated preference method choice experiment, where farmers were asked to choose between defined alternative contracts. In this study the farmers are presented with greening alternatives, and the conclusions are that farmers’ choices between greening alternatives are driven not only by the specific requirements of the greening measure, but also by personal and farm characteristics, and interactions between these two groups of variables. Farmers perceive “greening” as a costly constraint, but not all farmers are equally aﬀected and not all “greening” provisions are regarded as equally demanding.
Louhichi, et al. (2018) use the programming model IFM-CAP to analyse the uptake and effect of the greening requirements, including the use of catch crops as a greening measure. They conclude that eligibility and uptake of these measures depend largely on farm-specific characteristics (size, specialisation, location, etc.). The main finding from the modelling is that the effect of CAP greening on farm income seems to be small at the aggregate leve, but that there are large variations so at individual farm level, the impact could be more pronounced (e.g. a decrease of production and income of more than 30 per cent). They also conclude that the environmental effect seems to be modest.
Following this line of reasoning, and based on small greening effects seen in Spanish agriculture, Galán-Martín et al. (2015) suggest that CAP greening needs to be redefined and regionalised to ensure the transition towards a ‘greener’ agriculture.
Agri-Environmental Schemes (AES) as incentive for catch crop implementation
One type of economic incentives frequently used to implement abatement measures is AES, being studied in a large number of papers focusing on types of AES’s , aims of the AES and contractual obligations.
An increasing number of studies strive to understand the incentives for farmers to enter into voluntary programs. Studies are applied to a wide range of AES themes all over the world, for example
- rural development contracts (Villanueva et al. 2017),
- conservation and biodiversity (Broch et al. 2013; Adams et al. 2014; Allo et al.2015; Gomez-Limo´n et al. 2018),
- pesticide management (Christensen et al. 2011; Kuhfuss et al. 2016),
- land diversification requirements and production practices (Espinosa-Goded et al. 2010; Jaeck and Lifran 2014; Schulz et al.2014),
- carbon sequestration and climate mitigation (Aslam et al. 2017), and water quality improvements (Beharry-Borg et al. 2013; Franzén et al. 2016, Reiter et al 2016).
A number of studies focus on AES contract characteristics, for example
- contract length (Ruto and Garrod 2009; Christensen et al. 2011; Science for Environment Policy 2017, Hasler et al 2019)
- flexibility to end contract before planned time (Jaeck and Lifran 2014, Hasler et l 2019) and
- availability of advice for adoption rates (Ruto and Garrod 2009; Espinosa-Goded et al. 2010; Christensen et al. 2011; Mettepenningen et al. 2013, Hasler et al 2019).
Several of these studies apply stated preference methods (see Hanley and Czajkowski 2017 for an overview), as data on actual implementation typically does not permit identification of how contract characteristics affect barriers and potentials to enter into the AES (Zimmerman and Britz 2016). This can mainly be explained by the fact that contracts actually offered are not sufficiently heterogeneous with respect to contractual conditions, but also by the fact that data on actual implementation and the associated explanatory factors can be hard to sample.
Hasler et al (2019) is one example of a stated preference study, where farmers were asked to choose their most preferred AES contract. The farmers, selected from 5 Baltic Sea Countries (Denmark, Estonia, Finland, Poland and Sweden) were asked to choose between contracts for establishing catch crops, set aside and improved utilization of fertilizer. Catch crops are generally the most preferred measure among the three types of contracts, while fertilizer utilization tends to be the least preferred. Set aside is generally less preferred to catch crops across all 5 countries, with the exception of Sweden where set-aside contracts entail lower required subsidy level than catch crops contracts.
When the length of the contract for catch crops was increased in the experiments the farmers tended to increase their requirement for subsidy – in Sweden it was found that the length of the contract means that as additional payment of 15 EUR/ha has to be added for each year of extension. Among Estonian farmers, the contract length for catch crops was not significant for the accepted payment level. For all countries the farmers required payment levels were reduced if they received advice related to the implementation. In addition, the results indicate very high heterogeneity among farmers within each country. The required payment levels were lower for farmers without livestock compared to livestock farms, and farmers who leased the land required a higher payment level. These findings point at differences in potentials and barriers between farm types as well as between regions and countries.
Legal instruments include mandatory requirements, and this legal instrument is used in many European countries. Mandatory fertilization timing and storage capacity exist in almost all EU countries, while other measures such as mandatory catch crops are less frequent. These regulations exist in e.g. Denmark and Germany. More examples are provided for catch crops in »Detailed analysis of catch crops. The analyses and assessment in this section indicate that information retrieved from experts and farmers in the FAIRWAY survey slightly differs from results provided by cost-effectiveness modelling building on catchment data. This is worth noting, and has to be remembered when conclusions are drawn on which measures and governance structures are most favorable.
For full references to papers quoted in this article see