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The Relationship between Agriculture, Economic Activity, Settlement Patterns and River Water Quality 1991-2011
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 1991-2011
Summary StatisticsShare of Q-value 1-3
10
12
14
16
18
20
22
24
1985 1990 1995 2000 2005 2010 2015
Sha
re Q
V <
= 3
Downward trend in share of worst water quality over time
Trends in Water Quality (Share by Q-Value)
QV 1991 2002 2011
Worst 1.9 0.2 0.7
2 7.1 4.0 2.0
3 16.4 16.07 14.2
4 37.7 56.7 68.4
Best 26.9 23.0 16.6
Share unsatisfactory
25.4 20.3 16.9
Biggest Reduction in worst water quality areasHowever also a reduction in best areasGreater Bunching in Q-value 4
Trends in Intensity Variables (1991 – 100)
year Septic Tank Density Organic N per ha
1991 100.0 100.0
2002 116.5 94.7
2011 119.7 81.3
Increase in Septic Tanks Density over time, - but reduction in Organic N per hectareshift in intensities
Kernel Density – Organic N
0.0
05
.01
.01
5D
ensi
ty
0 50 100 150 200mned_org_N_perhet
2002 2011
kernel = epanechnikov, bandwidth = 5.3594
Kernel density estimate
Distribution of Organic N per ha across space between 2002-2011-Shifted to left, reflecting lower mean-More variable
Kernel Density – Septic Tank Density
0.0
2.0
4.0
6.0
8.1
Den
sity
0 10 20 30 40 50SepticTanksDensity
2011 2002
kernel = epanechnikov, bandwidth = 0.8739
Kernel density estimate
Little difference in distribution of Septic Tanks (except at tails)
Unsatisfactory Water Quality 1991-93 to 2009-2011
Fewer Unsatisfactory – particularly in SWSituation worse in NWConcentrations 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 1991 -ve Organic N per ha x 2011 +ve ** Cereal Share –ve** Cereal Share x 2002 +ve** Cereal Share x 2011 +ve**
Model 1: “Correct signs”, water quality worsens with more Organic N & Septic TanksModel 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 1991 -ve Organic N per ha x 2011 +ve ** Cereal Share –ve** Cereal Share x 2002 +ve** Cereal Share x 2011 +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 1991 -ve Organic N per ha x 2011 +ve ** Cereal Share –ve** Cereal Share x 2002 +ve Cereal Share x 2011 +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 2011 +ve Organic N per ha –ve ** Organic N per ha x 1991 -ve Organic N per ha x 2011 +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 2008. 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