The Future for Water QualityIan J. BatemanCSERGE, University of East Anglia, UK

Team members include: Eric Audsley, Sandra Barns, Ian Bateman, Roy Brouwer, John Crowther, Emma Coombes, Helen Davies, Brett Day, Amelie Deflandre, Silvia Ferrini, Carlo Fezzi, David Hadley,Danyel Hampson, Steph Hime, Mike Hutchins, Andy Jones, Dave Kay, Graham Leeks, Andrew Lovett, Colin Neal, Kerry Pearn, Paulette Posen, Dan Rigby, Daniel Sandars, Dawn Turnbull, Kerry Turner, Bruce Willoughby.

Water policy & environmental

change

Impacts

Outcomes

Modelling FarmLand Use &

Incomes

Farm income

Water quality

Costs Benefits

Spatial Cost-Benefit Analysis

Household welfare

Policy compliance testing

Modelling theWater

Environment

BenefitValuation

Water Water –– The Wider PictureThe Wider Picture

Market forces & ag. policy

The ChREAM team assembled Agricultural Census data for every 2km grid square, for all of England and Wales from 1969 to 2004 and combine this with over 50,000 farm years of data from the Farm Business Survey. This gives:

• Agricultural land use hectares (wheat, barley, grass, etc.);

• Livestock numbers (dairy, beef, sheep); etc.

We then add

• Environmental and climatic variables (rainfall, temperature, machinery working days, field capacity, etc.);

• Policy determinants (NVZ, NSA, ESA, Parks, etc.)

• Input and output prices for the period

Data and modellingData and modelling

Land use model subjected to actual versus predicted testing

Climate change simulationClimate change simulation

Modelling land use change as a result of:

• climate change;

• new policy;

• world market shifts;

• etc.

Also estimating resultant farm incomes

Nitrate leaching per month

Integrated modelling: Linking land use with diffuse water pollution

Land use change & water qualityLand use change & water quality

Nitrate leaching

per month

Modelling the impacts of land use change on river water quality and ecosystems services

- and how water policy forces land use to change

Land use change & water qualityLand use change & water quality

Dairy Farms (before)

Dairy Farms (after fert.

limit)

Policy change impacts on farm incomesPolicy change impacts on farm incomese.g. impact of a fertiliser limite.g. impact of a fertiliser limit

Profit (£ / ha)

Estimating policy impacts across Estimating policy impacts across an agriculturally diverse catchmentan agriculturally diverse catchment

The Yorkshire Derwent

Cost effectiveness of alternative policy tools: Cost effectiveness of alternative policy tools: Case study of the Derwent catchment Case study of the Derwent catchment

Baseline20% Fertiliser

reduction20% Livestock

reduction20% arable to

grass

N Concentration(mgN/L)

∆ ∆ ∆ ∆ Profit (£m)

Effectiveness(£/ha per mg/L)

-2.39

-0.25.4

£77

-0.3

-1.89

£43

-1.2

-5.53

£32

• Survey over 2,000 households

• Each home located

• Locate sites visited

• Use the water quality ladder to characterise each site

• Record visit frequency• Model trade-off between

visit frequency, visit cost and water quality

• Estimate the value individuals have for changes in water quality

Site 3Site 3

Site 2Site 2

Site 1Site 1

Site 4Site 4

Site 5Site 5

Valuing water quality improvementsValuing water quality improvements

(2 visits)(2 visits)

(4 visits)(4 visits)(6 visits)(6 visits)

(1 visit)(1 visit)

(1 visit)(1 visit)

• Supplemented by various ‘stated preference’ studies asking people about what changes they would value – and how much!

• Uses novel virtual reality choice experiment approach:

Valuing water quality improvementsValuing water quality improvements

No change in water bill £5 increase in water bill

Which do you prefer?Which do you prefer?

Summary: Modelling the full effects of policy, market or environmental change