The Relationship between Agriculture, Economic Activity, Settlement Patterns and River Water Quality Cathal O’Donoghue*, Cathal Buckley*, Aksana Chyzheuskaya*, Stuart Green*, Peter Howley**, Stephen Hynes***, Vincent Upton* * Teagasc Rural Economy and Development Programme ** University of York *** National University of Ireland, Galway Project Funded by EPA-Strive

Research Question  Relationship between economic drivers and water quality  Economic Drivers  Agriculture  Settlement and Water Treatment  Industrial Structure  Water Quality  Ecological Status  Environmental Context  Soils  Elevation  Weather

Literature  Local or Catchment Scale  O’Dywer et al, 2013  National Scale  Donohoe et al (2005)  Bivariate correlations  water chemistry  O’Donoghue et al (2010)  Ordered Probit  QValues  Curtis and Morgenroth (2013)  Lakes  Regression Model  Chemical measures  Not a static relationship  Research Gap  Trends over time

Drivers of Water Quality  Intensity  The intensity of activity such as the livestock density, the population density or the extent of septic tanks  Efficiency  The environmental efficiency in terms of the relationship between a given level of activity and water quality  Environmental Context  Local hydrological conditions

Data  Water Quality  Ecological status as measured by Q-Values collected by EPA  Q-values – ordered from 1 – Bad to 5 – High  Target to get to Q-Value 4-5 under Water Framework Directive  Economic Activity and Septic Tanks  Census of Population  Agricultural and Forestry Activity  Census of Agriculture  Forestry Service Data  Hydrological Characteristics  Teagasc Spatial Data Archive  Link spatial attributes to downstream water quality points  Study period

Summary Statistics Share of Q-value 1-3 Downward trend in share of worst water quality over time

Trends in Water Quality (Share by Q-Value) QV Worst Best Share unsatisfactory Biggest Reduction in worst water quality areas However also a reduction in best areas Greater Bunching in Q-value 4

Trends in Intensity Variables (1991 – 100) yearSeptic Tank DensityOrganic N per ha Increase in Septic Tanks Density over time, - but reduction in Organic N per hectare  shift in intensities

Kernel Density – Organic N Distribution of Organic N per ha across space between Shifted to left, reflecting lower mean -More variable

Kernel Density – Septic Tank Density Little difference in distribution of Septic Tanks (except at tails)

Unsatisfactory Water Quality to Fewer Unsatisfactory – particularly in SW Situation worse in NW Concentrations around Dublin, Coastal Towns, Border, W. Limerick, E. Donegal

Model Results – Basic Model  Model 1: Basic Model  Pseudo R2 0.35%  Septic Tanks –ve **  Organic N per ha –ve **  Model 2: Inter-temporal Model  Pseudo R2 1.76%  Septic Tanks –ve **  Organic N per ha –ve **  Organic N per ha x ve  Organic N per ha x ve **  Cereal Share –ve**  Cereal Share x ve**  Cereal Share x ve** Model 1: “Correct signs”, water quality worsens with more Organic N & Septic Tanks Model 2: No change in relationship with septic tanks, improves for given Organic N and Cereal Share

Model Results – Inter-temporal and Industry Models  Model 3 Inter-temporal Model + Industry  Pseudo R2 5.3%  Septic Tanks –ve **  Organic N per ha –ve **  Organic N per ha x ve  Organic N per ha x ve **  Cereal Share –ve**  Cereal Share x ve**  Cereal Share x ve**  Landfill within 3km – ve**  Cumulative Afforestation +ve**  Pigs per ha - ve  Poultry per ha - ve  Sectors (Industry, Commerce, Public Sector, Other) - ve** Model 3: Conclusions Robust to more sectors

Model Results – Inter-temporal and Environmental Models  Model 4 Inter-temporal Model + Industry + Environment (Soil, elevation, weather, X/Y)  Pseudo R2 9.3%  Septic Tanks –ve **  Organic N per ha –ve **  Organic N per ha x ve  Organic N per ha x ve **  Cereal Share –ve**  Cereal Share x ve  Cereal Share x ve**  Landfill within 3km – ve**  Cumulative Afforestation +ve**  Pigs per ha - ve**  Poultry per ha – ve  Sectors (Commerce) - ve** Model 3: Addition of environmental variables improve fit, but results robust

Model Results – Geographically Weighted Regression  There is an existence of spatial correlation  Use of GWR has a consistent story as OLS model however  Dependent Variable – Good Water Quality (QV 4-5)  Model 5: Inter-temporal Model + Environmental + County dummies  Pseudo R2 13.9% (Logit)  Septic Tanks –ve **  Septic Tanks x 1991 –ve**  Septic Tanks x ve  Organic N per ha –ve **  Organic N per ha x ve  Organic N per ha x ve **  Cumulative Afforestation +ve**  Pigs per ha - ve**  Poultry per ha – ve*  Sectors (Commerce, Other) - ve** Model 5: Conclusions Robust to more spatial correlation

Drivers of Improvement  The improvement in the relationship between Agricultural Activity and Water Quality is unsurprising given  Investment of €2.9 billion by farmers between 2005 and 2011 on improved facilities,  Improved farm management practices, including closed periods and minimum storage requirements  More efficient use of fertiliser,  Significant participation in Agri-Environmental improvement programmes and  Compliance with Nitrates Directives and compliance with cross- compliance measures within the Common Agricultural Policy.

Drivers of Improvement  It should be noted that  Environmental lag times are also quite long for practice improvement and investments to impact upon water quality, so it is expected that these investments will have a stronger impact into the future Many of the measures that improve water quality have the win-win of improving profitability.  Incentives created by public policy and the active participation by farmers  Have been instrumental in this improved situation.  Sustainable farm practice is a vital pillar in underpinning Ireland’s Green image that is central to the Food Harvest 2020 strategy

Policy Solutions  Data Challenges  Data QV value points only sampled once every 3 years variation due to weather over year  Agricultural Catchments – 6 catchments, with continuous monitoring  Weather  Hydrology  Stocking Rate  Measures have been successful on average  Challenge to target those areas with QV3  Maintaining QV5’s even more challenging  Improvements Localised not generally across country  Localised rather than national solutions?  More efficient to target problems rather than have a national solution  Farm Level MAC analysis  farms have their own MAC curves  Regulation may not be optimal.

Summary and Conclusions  Coefficient on Septic Tanks Constant over time, but density of septic tanks increasing  Contribution of septic tanks to water quality increasing  Coefficient on Organic N (Agriculture) falling significantly between 2000 and 2010 and density of Organic N falling  Production Function of Agriculture becoming more efficient  Tallies with policy and investment changes  Consistent with field studies  Lalor et al. (2010) report a reduction in soils with excessively high levels of P over that period;  At the national level, P fertiliser use has declined by 6 kg ha-1 (55 %) for grassland and 5 kg ha-1 (16-30 %) for arable crops between 2003 and  P-problem growing  The proportion of tested soils with excessive P (Index 4) has declined from 30 % to 22 % between 2007 and 2011 (Lalor et al., 2010), falling to 18% in 2012.

Thank You

Proving Green Credentials Ireland is in a good starting position… Context – Water Quality Source: European Commission, 2010 Share of Water Bodies Ground Water by mg/L – Ireland relatively strong – only 5 countries have a greater share of water bodies with < 40 mg/L. However mid- ranking in terms of <25mg/L

Proving Green Credentials Ireland is in a good starting position… Context – Water Quality (Freshwater Trophic Classes) Source: European Commission, 2010