Electronic copy available at: http://ssrn.com/abstract=1268357

Integrated Assessment of Water Framework Directive

Nitrate Reduction Measures

Carlo Fezzi1,2 Michael Hutchins3 Dan Rigby4

Ian J. Bateman3 Paulette Posen3 David Hadley3

19th August 2008

Abstract

The paper develops a spatially explicit method for integrated assessment of alternative measures to

reduce nitrate leaching into rivers and lakes from farms, a key objective of the European Union Water

Framework Directive (WFD). This approach combines regression models, based on Farm Business

Survey and June Agricultural Census data, for predicting the economic costs to agriculture of nitrate

reduction measures with a hydrological model encompassing both diffuse and point source pollution

to estimate the water quality changes arising from such instruments. A case study of the agriculturally

diverse Yorkshire Derwent catchment in the north of England illustrates the overall approach. We

consider three measures previously proposed to the UK Department for Environment, Food and Rural

Affairs (Defra) for tackling diffuse agricultural pollution: (i) reducing inorganic fertilizer application,

(ii) reducing livestock stocking rates and (iii) converting arable land to un-grazed grassland. The

results reveal marked variability in the economic impacts and nitrate leaching reductions, with the cost

effectiveness of these measures varying by up to a factor of three. Furthermore, the analysis suggests

that WFD implementation may entail major land use changes resulting in substantial economic

impacts. The spatially explicit aspect of our approach permits assessment of the optimal targeting of

policy implementation to areas of particular environmental interest.

Keywords: integrated assessment; Water Framework Directive; diffuse pollution, nitrates; cost-

effectiveness.

1 Corresponding author: c.fezzi@uea.ac.uk. 2 CSERGE, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ (UK). Ian Bateman is also Adjunct Professor in the Department of Agricultural and Resource Economics at the University of Western Australia, Perth and the Department of Economics, Waikato University. 3 Centre for Ecology and Hydrology, Crowmarsh Gifford, Wallingford, OX10 8BB (UK) 4 Economics, School of Social Sciences, University of Manchester, M13 9PL (UK).

Electronic copy available at: http://ssrn.com/abstract=1268357

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1. Introduction

The Water Framework Directive (WFD) requires member states of the European Union (EU) to

improve biodiversity in aquatic ecosystems and achieve “good ecological and chemical status” for all

water bodies by 2015. This goal will necessitate fundamental changes in the management of rivers and

lakes across Europe (European Commission, 2000). For the first time, water management is required

to be at a basin scale while quality targets are expressed not only in terms of the water chemical

composition but also with respect to biology and ecology and, therefore, considering of the aquatic

fauna and flora.

EU member states are currently formulating their implementation of the WFD. Indeed, the analysis of

the various options for putting into practice this major policy has been an important factor driving the

development of novel integrated hydrologic and economic models (Brouwer and Hofkes, 2008). For

example, the issues of irrigation and water scarcity in Spain have been addressed in Gómez-Limón et

al. (2002), Gómez-Limón and Riesgo (2004), Pulido-Velázquez et al. (2008), and different approaches

to model diffuse pollution abatement are implemented in Barton et al. (2008) for Norway, in Volk et

al. (2008) for Germany and Brouwer et al. (2008) for the Netherlands.

In the United Kingdom (UK), one of the major challenges for the implementation of the WFD is the

need to reduce diffuse pollution, the primary source of which is agriculture. For instance, roughly 70%

of the nitrate loads draining into UK water bodies are derived from farming activities (Environmental

Agency, 2002). Previous studies have identified a range of measures (e.g. cutting fertilizer application

rates, reducing livestock stocking rates, switching arable land to grassland) that could be adopted to

reduce diffuse water pollution from agriculture, estimating the economic costs using linear

programming (LP) models (UK Department for Environment, Food and Rural Affairs, Defra, 2007) or

farm account data (Fezzi et al., 2008). Despite being clearly informative, these contributions do not

take into account the spatial dimension of the problem, which, given the diversity of environmental

characteristics (including topographic, soil and climatic ones) and the agricultural practices that

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characterize a heterogeneous country such as the UK, is clearly substantial. More advanced, spatially

explicit integrated hydrologic and economic models, capable of capturing this heterogeneity and based

on detailed Geographical Information System (GIS) data, have been already implemented to evaluate

different diffuse pollution abatement options in other EU member states (e.g. Vatn et al., 1997, 1999,

2006; Brady, 2003; Brouwer et al., 2008, Pulido-Velázquez et al., 2008; Volk et al., 2008).

In this paper, we assess various options to reduce nitrate leaching from agriculture in the UK,

developing an integrated hydrologic and economic model which explicitly addresses the spatial aspect

of the issue. The economic model is based on the same assumptions of Defra (2007) and Fezzi et al.

(2008) and it is estimated using data across a sample of more than 2,000 farms from the UK Farm

Business Survey (FBS)5. The approach is based on simple econometric relations between economic

impacts and agricultural activities rather than on mathematical programming models involving

complex optimizations procedures which have been used in other WFD assessments (for example,

Barton et al., 2008; Pulido-Velázquez et al., 2008; Volk et al., 2008). A limitation of our method is

that it would struggle to assess the impact of certain micro-level policies (e.g. fencing exposed

waterways, changing manure applications, etc.) for which highly specialized data is required.

However, the approach presented here is very flexible and requires a minimum amount of data to be

extended to other geographical regions. Indeed, the statistical relationships developed in this study

between economic impacts and agricultural activities can be easily applied to any area for which the

land use patterns are known.

The hydrological model follows analogous assumptions and takes as a key input the pattern of

agricultural land use within an area (typically a river basin or a catchment). This land use data (which

can be the actual land use observed within a catchment or the land use arising from the

5 The FBS provides detailed information regarding the financial performance and physical and economic characteristics of UK farming enterprises, recording costs, output, revenues, machinery, labor, other assets, approximate location and physical characteristics for each sampled farm. Its sampling, which covers more than 2000 farms per annum, is designed specifically to inform policy decisions on matters affecting farm businesses and to enable analysis of impacts of policy options. The main publication from the FBS data analysis is the report on the Agriculture in the United Kingdom (Defra, 2005a).

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implementation of one the different measures as predicted by the economic model) is combined with

information on soil characteristics, rainfall and point source pollution to allow estimation of both

nitrate leaching and resulting water quality in terms of nitrate in-river concentrations.

The approach developed in this paper is illustrated through a case study of an agriculturally and

environmentally diverse area, the Yorkshire Derwent catchment in the north of England. We consider

three possible policy options among the ones proposed in Defra (2007) as potential measures to reduce

nitrate (N) leaching and achieve WFD targets6: (a) reducing inorganic fertilizer application, (b)

reducing livestock stocking rates and (c) converting arable land to un-grazed grassland. The approach

presented here comprises the following stages:

(a) Derive farm-specific economic impacts of implementing WFD measures using FBS farm

accounts data (summarized in Section 2);

(b) Generalize these results by regressing those costs on spatially distributed explanatory

variables such as land use and livestock numbers (Section 3);

(c) Specify the hydrological model which derives N leaching and concentrations based on

assumptions analogous to (b) (Section 4);

(d) Select a case study area (e.g. a catchment), simulate the implementation of WFD measures

in terms of costs and changes in N leaching and in-river N concentrations and combine

these to investigate cost effectiveness across and within the catchment (Section 5).

The first of these stages, the derivation of farm-specific economic impacts of implementing WFD

measures, is already completed work (Fezzi et al., 2008) and it is summarized in Section 2. The

contributions of this paper are to take these farm-level cost estimates from a nationally representative

sample and generalize them so that the costs of WFD measures can be estimated for any given river

basin or catchment. In addition the approach permits the predicted land use changes to be combined

6 Indeed, the research presented in this paper arises from a direct request from Defra to the authors for guidance regarding this issue.

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with the hydrological models to predict changes in leaching and nitrate concentrations and allow an

assessment of the cost-effectiveness of alternative WFD measures. As we illustrate in our case study,

the spatially explicit nature of the analysis allows us to investigate the potential for the optimal

targeting of policies within both the economic and hydrological dimensions. Our findings suggest that

achieving considerable improvements in the water quality within the Derwent would require

substantial land use changes, including switching a significant portion of arable cropping to un-grazed

grassland. Furthermore, we show how these measures can be spatially targeted in order to maximize

the water quality improvements in areas of particular environmental and recreational interest within

the catchment.

2. The economic impact of WFD measures on farms

Among the potential policies proposed in Defra (2007) to achieve the targets of the WFD in the UK,

we consider three possible measures: (a) reducing inorganic fertilizer application, (b) reducing

livestock stocking rates and (c) converting arable land to un-grazed grassland. We assess their

economic impact in terms of changes in farm gross margin (FGM), defined as the difference between

revenues arising from the different activities carried on within the farm and variable costs. Fezzi et al.

(2008) derive the estimates of those impacts through a farm account approach based on simple

assumptions, consistent with previous literature on the UK farming system (in particular Defra, 2004a,

2007). For each policy measure, they derive changes in variable costs (e.g. fertilizer, feedings,

pesticides, etc.) and revenues (including both crop and livestock production) which, computed for all

the farms included in the Farm Business Survey of year 2005 (roughly 2,300 farms sampled across the

whole country), allows the calculation of the resulting changes in farm gross margin. That analysis,

encompassing the heterogeneity in farm size, location and technology employed in the UK agriculture,

reveals how the economic costs of a series of WFD policy measures may differ significantly across

and within farm types, according to the individual characteristics of each farm. For this reason, the

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underlying variability in the economic impacts hidden by an “average” measure might be substantial.

This assumes important policy implications considering the extensive concern regarding rural poverty

and the decline in agricultural incomes in the UK. It is particularly relevant for the implementation of

the WFD, considering that articles 4.4 and 4.5 of the Directive allow exemptions if the measures

required to achieve the policy objectives are expected to generate “disproportionate costs”.

[Table 1 about here]

Farm gross margin (FGM) (£) and changes in FGM (£) arising from the different policy options

considered in this analysis.

Table 1 and Figure 1 briefly illustrate the results in Fezzi et al. (2008) which are relevant for this

analysis, being the underpinning data of our econometric approach7. Table 1 reveals that the impact of

the policy measures in terms of changes in farm gross margin (∆FGM) is substantial. For example,

considering the fertilizer reduction measure, the 90% confidence interval for the predicted ∆FGM

ranges from roughly a loss of £10,500 and a gain of £200 per year. Farms heavily affected are

typically large dairy and livestock farms, since their output is strongly associated with grass

production and fertilizer application. On the other hand, a minority of crop farms actually achieve

small gains from this measure, because of initial over fertilization of fields. In contrast, as one would

expect, switching 20% of the arable land to grassland is the measure that produces the highest impact

on cropping farms, particularly on the ones located in more favorable areas, with typically higher

proportions of land devoted to vegetables, potatoes or sugar beet. Altogether, the majority of farms

suffer losses as a result of the three measures considered here, these being highly variable both across

policies and particularly across farms.

[Figure 1 about here]

7 For a detailed description of the modelling assumptions, the data, the results and the policy implications of the analysis in Fezzi et al. (2008) we suggest the interested reader to refer to the original text.

7

Changes in FGM/ha from a 20% reduction in fertilizer application in cropping, dairy and grazing

livestock farms.

This heterogeneity characterizing UK farms, even when belonging to same broad category (e.g. dairy,

cropping, etc.), is evident from Figure 1, which displays the kernel density of the changes in farm

gross margin per hectare (∆FGM/ha) arising from the 20% fertilizer reduction policy as predicted in

Fezzi et al. (2008). Dairy farms are characterized by both the highest average impact and the highest

variance of impacts, ranging from more than £250/ha to almost no impact, whereas cropping farms

have both the lowest average impact and the lowest variability. However, the overlap between the

impact ranges is considerable. This significant variability is the result of the data comprising real

farms, often with a mix of enterprises in a range of diverse conditions.

3. Regression models for transferring policy impacts to a spatial level

In this section we model the significant heterogeneity in estimated WFD impacts illustrated in the

previous section. This is done by estimating regression models which relate the economic impact

arising from each policy option derived in Section 2 to the agricultural activities carried out on the

farm as follows:

∆FGMj,k = β 1,k x1,j + β2,k x2,j + …+ βn,kxn,j + uj,k , (1)

where k = 1,..,3 is the measure considered, xi,j (i = 1,...,n) is the land use activity (e.g. hectares of

potatoes) or the livestock number (e.g. number of dairy cows) in farm j =1,…,N. This model implies

that the change in FGM arising from implementing one policy option (e.g. reduction in fertiliser

application rates) is a linear function8 of the land use and animal stock on the farm plus a residual,

farm specific, error component uj,k. There is no constant in the model since all the farm activities are

8 As showed later, the linear specification provides an excellent fit to the data and, therefore, we did not find necessary to test for other possible functional forms.

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included in the xj terms, i.e. when a farm has neither any cultivated area nor any livestock, the mean

effect of a policy is zero. The residual component, assumed to be Gaussian distributed, with mean zero

and diagonal covariance matrix, includes all the variability in ∆FGM in farm j associated with policy k

that cannot be explained by the farm activities. For example, it encompasses differences in farming

practices, soil, crop qualities and intensities. The parameters of this model are estimated using

nationally representative FBS data for 2005, the most recent year available, comprising 2172

observations.

The econometric equations (1) are readily applicable to predict the ∆FGM arising from a policy for

any specified area for which land use data are available. As spatially referenced data are held for all of

the UK within the June Agricultural Census, the regression approach is applicable to any location and

any specified area of land within the country9. Estimation of the parameters in (1) and their use for

prediction, particularly on a catchment scale, deserves some additional considerations requiring

extensions to a conventional OLS regression model. These matters are discussed below.

The first issue concerns the hypothesis of linearity. Equation (1) clearly entails that the relationship

between ∆FGMs and the land use variables is linear. This implies the assumption of constant returns

to scale. Since the analysis considers ∆FGMs, where only variable and not fixed costs are considered,

this assumption is not particularly severe. For example, assuming a decrease in livestock numbers of

20% will imply, in the short run, a 20% decrease in the gross margins on that activity, regardless the

farm dimension, appears to be reasonable (and indeed that is assumed, for instance, in Defra, 2007).

The second issue concerns the residual component ui, which includes all the factors explaining

∆FGMs that have not been included in the model (crop qualities, soil types, management practises,

etc). The implicit assumption of homoskedasticity (i.e. σ2 constant for all farms) appears more difficult

9 However, caution should be used when applying these regressions to areas characterized by very atypical conditions. For example, some uncommon terrain types might not feature in the FBS data and, therefore, the relationships estimated on the latter might result in not being an accurate description in these peculiar circumstances.

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to justify, since the variance of the residual component might be related, inter alia, to the farm size

and type. We conduct two tests of homoskedasticity after a preliminary estimation of equation (1) via

OLS: the White (1980) test against an unknown source of heteroskedasticity and the Brown and

Forsythe (1992) test to assess if the variance of ui differs across farm types. Both tests strongly reject

the null hypothesis of homoskedasticity. Therefore, we address this issue implementing two

complementary approaches: (a) we estimate the parameters using Weighted Least Squares (WLS) and

(b) we implement the robust parameter covariance matrix introduced by White (1980), with the finite

sample correction proposed in Davidson and McKinnon (1993), to encompass any un-modelled source

of variance.

The WLS estimation involves three steps (see, for example, Greene 2003, Chapter 11): estimating the

model using OLS, regressing the squares of the OLS residuals (a consistent estimate of the variance

σi2) on a set of variables explaining the heteroskedasticity and, finally, using the fitted values in the

auxiliary regression as weights to implement the WLS estimation of the model.

The specification of the functional form of the auxiliary regression, in which the squares of the OLS

residuals are the dependent variable, is the main issue (Ryan 1997, Chapter 2.1.3; Greene, op.cit.). In

this context, we adopt a multiplicative structure, as suggested in Greene:

∏=

=5

1

0

2 ,

t

d

tiiitEESU αασ

α , (2)

where dt,i (t = 1,…,5) are dummy variables identifying the different farm types, ESUi is the size of the

farm in European Size Units and the α terms are the parameters to be estimated. This specification

implies that when the farm size is zero there is no residual variance left. Furthermore, as the farm size

increases, the residual variance can increase proportionally (if αE = 1), more than proportionally (αE >

1) or less than proportionally (αE < 1). If αE is not significantly different from zero, the variance of the

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residual does not depend on the farm size. This approach provides a flexible residual variance

structure and allows us to compute a more precise prediction interval.

The descriptive statistics of the explanatory variables used in the model are reported in Table 2

(whereas the ones of the endogenous have been already presented in Table 1). For all variables, the

standard deviations are consistently higher than the respective means, highlighting the significant

variability which characterizes this data. Indeed, the considerable heterogeneity is one the most

remarkable feature the UK farming system. For instance, all the potatoes and sugar beet production is

concentrated in less than 10% of farm within the sample, and the same is true for pigs and poultry

enterprises.

[Table 2 about here]

Descriptive statistics of the regressors in equation (1).

Table 3 reports the final WLS parameter estimates of the three regression models relating farm

activities to the impact upon FGM of each nitrate reduction policy. As the table makes clear, all the

models provide an excellent fit to the data. The coefficients indicate how much the associated farm

activity is estimated, on average, to change FGM under a certain policy. For example, for each hectare

of cereal switched to un-grazed grassland there will be, on average, a decrease in FGM of £290. Those

parameters that are not significant have been excluded from the model. A variable may be estimated of

being not significant in determining a policy impact for a number of reasons. The measure may not be

targeted to that specific land use or livestock type (e.g. a livestock reduction is, clearly, not targeted to

crops) or it may be that the changes in revenues and costs associated with a measure, on average,

balance each other (e.g. other arable crops with a 20% cut in fertiliser application).

[Table 3 about here]

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WLS regression estimates (dependent variable = ∆FGM, 95% confidence intervals in brackets)

Interestingly, reducing fertiliser application by 20% produces, on average, no changes in FGMs for

most of the arable crops, including cereals. These findings are based on the baseline fertilization levels

assumptions in Defra (2005b), which in turn imply that arable farms are slightly over-fertilizing their

fields10. The impact of reducing fertilizer application by 20% on livestock farms is however severe

with an estimated decrease in FGM of approximately £90 for each dairy cow within the herd.

Considering the livestock reduction policy, in line with farm management data (e.g. Nix, 2004), it is

significantly more costly, in terms of reduction in FGM, to reduce the herd of a dairy enterprise than

that of a beef one. Finally, when considering measures involving the switching of land to un-grazed

grassland it is possible to derive a switching order from the coefficients in Table 3. This implies that

land under oilseed rape and cereals would be the first to be switched to grassland and that the one

under potatoes the last one.

4. Hydrological modelling

The nitrate flow modelling involves the integration of two hydrological models: CASCADE

(CAtchment SCAle DElivery), a diffuse pollution model, and QUESTOR (QUality Evaluation and

Simulation TOol for River-systems) an in-stream water quality model. The integration between those

two models has been already investigated in previous work (e.g. Hutchins et al. 2006).

The first element of the model, CASCADE, is a spatially distributed water quality model operating at

daily time-step, which combines land use data (which we again obtain from the sources detailed

previously) with information on physical parameters such as soil type, geology and rainfall (obtained

from a variety of geographical information system (GIS) based resources; see Hutchins et al., 2006).

10 However if the cuts in fertilizer application increase we would expect a significant decrease in FGM, bearing in mind that the relationship between arable yield and fertilizer application is typically non-linear and concave (see, for example, Defra, 2000).

12

CASCADE predicts the flow of water and pollutants from each hydrological response unit (HRU)11 to

determine the deposition of nutrients into the waterways. Sources of pollution considered include

nitrogen fertilizers, organic wastes and atmospheric deposition. The model estimates the nitrate

flowing into the river from each HRU considering a monthly profile based on land use type, soil

organic matter content and fertilizer regimes (quantity and timing of fertilizer application). Consistent

with the economic model, the farms are assumed to be applying fertilizer rates in line with Defra

(2005b). The reduction in N leaching associated with a 20% decrease in the fertilizer application and a

20% decrease in the livestock number are based on Defra (2000) and previous research (Lord 1992,

Jarvis et al. 1995, Shaffer 2002).

The second model element, QUESTOR, relates N loads to concentrations. QUESTOR combines the

diffuse output from CASCADE with point source (e.g. from sewage treatment works and industry)

and abstraction data, and models in-stream processes to produce estimates of nitrate concentration

within each reach of the river network (details are given in Hutchins et al., 2006). Therefore, moving

from loads to concentration introduces in the analysis environmental factors, namely the diluting effect

of rainfall, the impact of mixing with flows from the other diffuse and point sources in a catchment,

and the effect of in-river sources and sinks of nitrate. While previous studies have focused upon the

issue of load (Defra 2004a, 2007), it is the in-stream concentration which directly bears upon resultant

ecological status, the primary objective of the WFD (see Triest et al. 2001, Nijboer and Verdonshot

2004).12Consequently, while for comparability purposes we report loads, we place greater emphasis

upon predicted concentrations (although within the current case study these are substantially correlated

with each other).

11 Hydrological response units (HRU) are units of land hydrologically independent to each other, each draining directly into a river reach. The spatial extent of each HRU is determined by topography, with size typically ranging between 2-8 Km2. 12 The present application derives nitrate leaching reduction estimates in terms of eventual steady state conditions. It should be noted that these can take a long time to be attained, especially in groundwater-fed rivers. Such problems may undermine the time-dependent aims of the WFD which requires movements to good ecological status by 2015. It seems unlikely that such a schedule can be achieved within some long-residence groundwater areas.

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5. Integrating economic and hydrological assessments of nitrate leaching

reduction policies: a case study

In this section we outline the process by which the regressions estimated previously are used to derive

the economic impacts of nitrate reduction policies in specific areas in which land use patterns are

known. The resulting cost estimates are then combined with associated nitrate load and concentration

reductions derived from our hydrological models. Prior to this, we briefly describe the characteristics

of the case study area.

4.1 The Derwent catchment

The Yorkshire Derwent catchment, in the North East of England, covers an area of approximately

1600 Km2 and includes the rivers Derwent, Rye, Dove, Hertford and their tributaries. As shown in

Figure 2, most of the land is devoted to agriculture which consequently provides the major

contribution to nitrate in local rivers. The catchment includes a wide range of farm types, with its

north-west dominated by grazing livestock enterprises while the south-east largely devoted to crop

cultivation.

[Figure 2 about here]

The Yorkshire Derwent and its sub-catchments

The Derwent also contains two priority areas identified under the Catchment Sensitive Farming

Programme (Defra, 2004b); the lowland eastern part of the Derwent (Low Marishes) and the River

Rye sub-catchment in the north-western upland area. This programme aims to reduce diffuse water

pollution from agriculture to meet WFD requirements by promoting farmers to adopt voluntary

measures in 40 priority catchments. Given this pre-existing prioritization, alongside an analysis of the

impacts of applying the selected policies across the full extent of the catchment, we also investigate

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the effect of a more spatially targeted approach in these sub-areas. Figure 3 indicates these various

areas and also displays the location of the 5 Environment Agency (EA) monitoring sites for which our

hydrological impact estimates are generated and compared with the baseline concentrations.

[Figure 3 about here]

Derwent sub-catchments and monitoring sites

Livestock numbers and the agricultural land uses within the Derwent are derived from June

Agricultural Census data 2004 updated using the Land Cover Map 2000 and OS Meridian data (the

latter permitting the identification and removal of developed and urban land). Overall land use patterns

are reported in Table 2 along with data for the upland and lowland sub-catchments identified

previously. Along with Figure 2, this data reveals a wide variety in agricultural land uses and their

location. Grassland is the largest single land use type for the whole catchment, accounting for 36% of

the overall area and 55% of the upland region which hosts a significant number of livestock,

particularly beef and sheep. Cereals dominate the lowland area, covering 39% of the land compared to

20% for grassland. The two sub-catchments of interest for our spatially targeted policy implementation

(namely the Upland NW and the Lowland E) differ also in their hydrological characteristics: the east is

characterized by higher groundwater contribution, which moderates and delays the impact of changes

in land management more than in those areas dominated by surface water. These are reflected in

values of the Base Flow Index (CEH, 2003) as defined by Gustard et al. (1992). This heterogeneity

further motivates our analysis of spatially targeted policies. In this way the effects of any land-use

change in river nitrate concentrations are tempered by the ongoing influence of historic land-use. In

groundwater-fed rivers this is particularly relevant.

[Table 4 about here]

The Derwent: agricultural land use and livestock numbers

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4.2. Integrated hydro-economic assessment of nitrate mitigation options for the Derwent

The approach and models derived outlined previously are now used to assess the cost-effectiveness of

selected policy options aimed at reducing N concentration within the Derwent. The economic impact

of each policy for agriculture, in terms of ∆FGM, is computed using the estimated WLS regressions in

Table 3. This is achieved by using GIS to extract data on the values of all the explanatory variables in

the regression models across the study area. The same data, derived on a HRU basis, is the main input

of the hydrological model, employed to predict water quality changes at a sub-catchment and at a

catchment level.

As indicated previously, we consider the implementation of the following potential WFD measures

within the Derwent catchment: (a) reducing inorganic fertiliser application by 20%; (b) reducing

overall livestock stocking rates by 20%; (c) switching 20% of the total arable land (including cereals,

oilseed rape, sugar beet, potatoes and other cereal for a total of roughly 115 Km2) to un-grazed

grassland.

Our spatially explicit methodology allows us to formulate this latter policy in three different ways.

First, the 20% switch is distributed evenly across the entire catchment. Second, the same area of land

is switched but all the change is confined to occur purely within the lowland Eastern sub-catchment.

Third, the same area of land is again switched but now all the change occurs in the North-West area.

In both the hydrological and economical model we evenly spread the land conversion across all the

arable categories within the catchment.

The estimated nitrate load and concentration levels at each of the EA monitoring sites (shown in

Figure 3) are given in Table 5 for each policy option. Compared to the conversion of arable land to

grassland, the policies to reduce fertilizer or livestock rates have relatively little impact on both nitrate

fluxes into rivers and nitrate concentrations (of these latter two policies the livestock reduction

performs marginally better).

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[Table 5 about here]

Baseline and change in annual diffuse N loads (Kg/ha) and mean nitrate-N concentrations (mg/L) in

monitoring sites for each policy option

The last three columns of Table 5 show the effects of land use changes from arable cropping to un-

grazed grassland systems. This policy delivers relatively substantial reductions in mean N

concentration and loads. In terms of N levels at the catchment outlet (site 5) the most effective strategy

overall is to spread the land conversion evenly across the entire catchment (which reduces the nitrate

concentration at the outlet by 21%). However, focusing land use change in those areas highlighted by

the Catchment Sensitive Farming Programme generates reasonably comparable outlet reductions,

while addressing the priorities identified in that programme by delivering even more significant nitrate

concentration reductions in those key upland and lowland areas.

Table 6 brings together our economic and hydrological analyses. This shows that the fertilizer and

stocking rate reductions have significant economic impacts (between about £1.5m and £2.5m).

Furthermore, in terms of cost-effectiveness, defined as the ratio of catchment aggregate economic

impact (£) to the reduction in N mean concentration at the catchment outlet (mg), these measures are

the least effective (even though reducing livestock stocking rates is much more efficient than

decreasing fertilizer application). Switching arable land to un-grazed grassland imposes even higher

costs on agriculture in the catchment, varying between £5.2 and £5.8m, depending on the precise

formulation of the measure. However, the substantially greater degree of nitrate reduction generated

by these measures results in them being more cost-effective solutions. These results are somewhat at

odds with earlier work (Defra 2004a, 2007) which suggested that fertilizer and livestock reductions

might be reasonably cost effective. In part this may be generated by of the fact that our analysis

focuses on a specific catchment. However, methodological differences may also be pertinent here.

While those optimization approaches focuses upon representative farms, the present methodology

17

reflects the real world variation in environmental and farming condition captured in the FBS and

Agricultural Census data. Both factors are likely to be contributing to this difference in assessments.

[Table 6 about here]

Changes in N loads and concentrations at the catchment outlet, total annual ∆FGM and cost

effectiveness for each policy option.

6. Conclusions

EU member states are currently formulating their implementations of the WFD. This will involve

point and diffuse source pollution mitigation strategies. This study represents a spatially explicit

approach that allows us to generate estimates of the costs to agriculture for any area within which

agricultural land use patterns and livestock numbers are known. The farm level economic impacts of

nitrate leaching reduction measures are modelled as a function of land use and livestock variables.

Combining those regression models with agricultural land use and activities data for specific

catchments or river basins allows us to derive changes in Farm Gross Margins corresponding to the

different policy options implemented in those regions.

These transferable models of the agricultural cost of WFD policies are integrated here with a

hydrological analysis which allows the estimation of spatially distributed reductions in nitrate loads

and concentrations in different monitoring sites within a catchment. This integrated assessment of

costs and nitrate reductions allows evaluation of spatial cost effectiveness. Such an approach is

particularly relevant given the WFD’s explicit reference to “disproportionate costs” among the

possible reasons member states may seek derogations or exceptions from the achievement of good

status by 2015. Recognizing that the damage caused by nitrate pollution (and the benefit of its

removal) might well not be uniform (e.g. the value of nitrate reductions in areas of high recreational

18

value may outstrip those in remote locations), we also consider spatially differentiated

implementations of measures.

Our approach is illustrated using three different WFD measures (reducing fertilizer application,

reducing livestock stocking rates, switching arable land to grassland) applied to an agriculturally and

geologically diverse river catchment, the Yorkshire Derwent. Findings suggest that achieving

considerable improvements in the water quality within the Derwent using one or a combination of the

selected measures would require substantial land use changes, including switching a significant

portion of arable cropping to un-grazed grassland. Such implementations would incur substantial costs

to agriculture, yet are more cost effective than reducing fertilizer application or livestock numbers.

These findings regarding WFD requirements pose a particular problem given the changed economic

conditions of the last two years. Soaring food prices have shifted a policy agenda that was increasingly

post-productivist and focusing on the non-market outputs of agriculture, such as biodiversity, to again

an enhancement of production, as witnessed by the setting of the 2008 set aside rate at 0%. This

expansion of land farmed intensively suggests a potential conflict with the emerging imperatives of the

WFD implementation. This conflict may be ameliorated by a spatially sensitive implementation of the

WFD which seeks to minimize the costs to agriculture for a given improvement in water quality. In

this context, the spatially explicit methodology presented in this paper permits the examination of the

targeting of policies to different areas of particular environmental and recreational interest. This

analysis suggests that targeting is feasible and may allow the simultaneous achievement of multiple

policy goals.

There are important areas in which the current work will benefit from further research. Firstly, only

gross margins are considered here, thereby providing no indication about profits and long run

investment costs. An obvious candidate for such development is therefore extending this analysis to

include profits and long run considerations. Furthermore, the behavioral aspect of the model could be

developed further, since at present the various measures are implemented in a rather mechanical

fashion. For example, the restrictions on stocking and fertilizer application rates result only in less

19

intensive versions of the existing activities, rather than switches between activities. Refining this

implementation via a more sophisticated range of behavioral responses to such measures will further

strengthen the analysis and it is a goal of ongoing research. On the hydrological side, since the WFD

targets are based on the biological state of river ecosystems, an obvious extension would be to link

nutrients concentration with indicators of biodiversity and species abundance and, therefore, assess the

effectiveness of WFD measures in terms of their biological consequences.

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22

Figure 1: Changes in FGM/ha from 20% reduction in fertilizer application in cropping,

dairy and grazing livestock farms.

Source: Fezzi et al. (2008).

23

Figure 2: The Yorkshire Derwent and its sub-catchments

Sources: EDINA, (http://edina.ac.uk), UKBORDERS and OS Meridian 2, 1:50000 data sets © Crown Copyright and Land Cover Map 2000 from the Centre for Ecology and Hydrology.

24

Figure 3: Derwent sub-catchments and monitoring sites

Sources: EDINA, (http://edina.ac.uk), UKBORDERS and OS Meridian 2, 1:50000 data sets © Crown Copyright.

25

Table 1: FGM (£) and changes in FGM arising from the different policy options considered in

this analysis.

Baseline 20% FR 20% LR 20% SW

Mean FGM 69,300 58,900 58,700 58,800

Mean ∆FGM -4,000 -4,100 -4,000

10th per. ∆FGM -10,500 -12,700 -10,900

90th per. ∆FGM 200 0 0

Notes: Baseline = year 2005 FBS data, FR = fertilizer reduction, DC = diet change, LR = livestock reduction, SW = switching from arable to grassland. Source: Fezzi et al. (2008).

26

Table 2: Descriptive statistics of the regressors in equation (1).

xi mean 10th perc 90th perc dev. standard

cereals (ha) 35.95 0.00 111.95 81.0

potatoes (ha) 1.70 0.00 0.00 11.6

sugar beet (ha) 2.37 0.00 0.00 11.0

oilseed rape (ha) 7.98 0.00 25.65 27.7

other arable (ha) 1.33 0.00 0.00 8.6

vegetables (ha)

5.65 0.00 15.35 26.7

dairy (num) 26.8 0.00 109 68

beef (num) 77.8 0.00 197 103

sheep (num) 271.2 0.00 856 495

pigs (num) 84.3 0.00 0.00 557

poultry (num) 1,954 0.00 0.00 17,701

Note: FBS data for year 2005, farms classified as horticulture and with less than 1 European Size Unit excluded. 2172 farms in total remained.

27

Table 3: WLS regression estimates (dependent variable = ∆FGM, 95% confidence intervals in brackets)

WFD Policy Measure

xi

20% Fertilizer

cut

Livestock

reduction

Arable to

grassland

Cereals (ha)

-- --

-- --

-290 [-313 ; -266]

Potatoes (ha)

- 153 [-180 ; -127]

-- --

-3440 [-3970 ; -2910]

S. beet (ha)

72 [60 ; 85]

-- --

-1410 [-1550 ; -1270]

OS rape (ha)

-- --

-- --

-310 [-367 ; -252]

Oth. Arable (ha)

-- --

-- --

-425 [-611;-240]

Vegetables (ha)

- 46 [-69 ; -23]

-- --

-- --

Dairy (n)

- 92 [- 95 ;- 89]

-576 [-595;-558]

-- --

Beef (n)

- 16 [-17 ; -14]

-69 [-61 ; -53]

-- --

Sheep (n)

-2.6 [-3.0 ; -2.3]

-9.3 [-11.4 ; -7.1 ]

-- --

Pigs (n)

-- --

-- --

-- --

Poultry (n)

-- --

-- --

-- --

Observationsa

2132 2093 2115

( )kFGMs ∆ 7331 8011 10507

( )kus 1911 2159 1453

R2 0.93 0.91 0.96

a The number of observations varies because of the exclusion of outliers.

28

Table 4: The Derwent: agricultural land use and livestock numbers.

Derwent Upland (NW) Lowland (E)

Land use (ha, %)

Urban 7,520 5.1%

1,890 2.9%

2,540 7.0%

Woodland 19,140 13.0%

10,480 16.3%

3,925 10.9%

Cereals 42,245 28.7%

10,550 16.4%

13,945 38.6%

Potatoes 2,810 1.9%

625 1.0%

1,050 2.9%

OS Rape 6,320 4.3%

1,005 1.6%

2,420 6.7%

Sugar Beet 1,950 1.3%

170 0.3%

700 1.9%

Other Arable 2,430 1.7%

560 0.9%

875 2.4%

Vegetables 150 0.1%

35 0.1%

90 0.3%

Set aside 6,770 4.6%

1,645 2.6%

2,360 6.5%

Grasslanda 52,575 35.8%

35,240 54.8%

7,150 19.8%

Total 147,010 100%

64,330 100%

36,130 100%

Livestock (No.)

Dairy 7,060 2,670 1,550

Beef 50,310 21,350 14,080

Sheep 248,940 161,950 34,410

Pig 108,480 21,770 28,710

Poultry 468,490 149,470 138,670 a Includes both temporary grassland and rough-grazing areas.

29

Table 5: Baseline and change in annual diffuse N loads (Kg/ha) and mean nitrate-N concentrations

(mg/L) in monitoring sites for each policy option.

Baselinea 20%

Fertilizer reduction

20% Livestock reduction

Convert arable to grassland (evenly spread)b

Convert arable to grassland (focus on lowland)b

Convert arable to grassland (focus on upland)b

Site

Levels:

1

load

concentration

19.0

3.70

-1.1 -6%

-0.21 -6%

-2.0 -11%

-0.38 -10%

-2.8 -15%

-0.54 -15%

-- -- -- --

-11.4 -60%

-2.19 -59%

2

load

concentration

16.1

2.89

-1.1 -7%

-0.19 -7%

-2.1 -13%

-0.37 -13%

-2.1 -13%

-0.40 -14%

-- -- -- --

-8.5 -53%

-1.51 -52%

3

load

concentration

18.4

4.49

-0.8 -4%

-0.19 -4%

-1.6 -9.%

-0.34 -8%

-3.0 -16%

-0.71 -16%

-- -- -- --

-12.1 -66%

-2.88 -64%

4

load

concentration

24.7

6.28

-0.8 -3%

-0.19 -3%

-1.0 -4%

-0.25 -4%

-6.6 -27%

-1.47 -23%

-14.2 -57%

-3.25 -52%

-- -- -- --

5

load

concentration

24.9

5.40

-1.1 -4%

-0.21 -4%

-1.5 -6%

-0.30 -6%

-5.5 -22%

-1.14 -21%

-4.1 -16%

-0.81 -15%

-5.0 -20%

-0.85 -16%

a Reference values are absolute annual loads and concentrations simulated by the model for 2000-03 climate inputs and land use, assuming the baseline level of fertilisation in Defra (2005). All scenarios assume that the baseline climatic conditions apply. b Arable to grassland (evenly spread): Converts 20% of total Derwent arable area to grassland; Arable to grassland (focus on lowland): Same land area of arable switched to grassland but all switching occurs in the lowland (E) area; Arable to grassland (focus on upland): Same land area of arable switched to grassland but all switching occurs in the upland (NW) area. Blank entries denote no change in N.

30

Table 6: Changes in N loads and concentrations at the catchment outlet, total annual ∆FGM and cost

effectiveness for each policy option.

Baseline

20% Fertilizer reduction

20% Livestock reduction

Convert arable to grassland (evenly spread)

Convert arable to grassland (focus on lowland)

Convert arable to grassland (focus on upland)

Load

24.9

-1.1 -4%

-1.5 -6%

-5.5 -22%

-4.1 -16%

-5.0 -20%

Concentration 5.40 -0.21 -4%

-0.30 -6%

-1.14 -21%

-0.81 -15%

-0.85 -16%

∆FGM (£m) -2.39

[-2.50;-2.27] a

-1.89 [-2.00;-1.79]

-5.53 [-5.84;-5.23]

-5.53 [-5.84;-5.23]

-5.35 [-5.68;-5.02]

Cost effectiveness (£/mg)b

-11.3

[-11.9;-10.8]

-6.3

[-6.6;-6.0]

-4.8

[-5.1;-4.6]

-6.6

[-7.2;-6.4]

-6.2

[-6.7;-5.9] a Figures in parentheses indicate the 95% prediction interval; b Cost effectiveness given by the ratio of catchment aggregate economic impact (∆FGM) to the reduction in N mean concentration at the catchment outlet. The figures in the square brackets are the extremes of the ∆FGM 95% prediction intervals divided by the expected reduction in N concentration.