Cattle Exclusion: Policy Effectiveness,

Adoption and Implications

Paul Kilgarriff

Co-authors: Mary Ryan, Stuart Green, Cathal O’Donoghue,

Daire Ó hUallacháin

166th EAAE Seminar on Sustainability in the Agri-Food Sector, NUI Galway.

30th August 2018.

This work was carried as part of the Teagasc Cosaint Project (Cattle Exclusion from Watercourses) which is

funded by the Environmental Protection Agency (EPA)

https://www.teagasc.ie/environment/biodiversity--countryside/research/current-projects/cosaint/

Overview§ Creation of a new national farm

boundary layer§ Using boundary layer for policy analysis§ Creation of new river boundary layer§ Extent of on-farm watercourses§ Costs and benefits§ Spatial clustering

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Data§ Datasets:

• LPIS (Land Parcel Identification System) – Agriculture farm parcels – collected for administration purposes

• OSi (Ordnance Survey Ireland) PRIME2 – Field polygons, river polygons

• OSi River Network (EPA website)

§ Using GIS (Geographic Information System) -Match, merge, join datasets together to produce dataset containing farm holdings – subdivided into individual fields

§ Dataset can be queried to answer a variety of questions

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LPIS match with PRIME2§ LPIS parcels are fields which have been grouped together based on

crop and ownership• Adjacent fields used for grazing appear as one LPIS parcel

• LPIS contains no information on farm boundaries

• LPIS version is historic data received from the EPA

§ Land eligible, ineligible and claimed land

• Arable crops, permanent grassland, new afforestation eligible

• Buildings, scrub, marsh land ineligible

• In some instances the total area of a LPIS parcel may not be claimed but polygon boundary exists

§ Using OSi PRIME2 data, enables these parcels to be divided into individual fields but retain information from LPIS

§ Matching LPIS to Prime2 will remove these discrepancies

§ More spatially accurate4

§ Individual farms identified for the first time

§ Prime2 field boundaries linked to farm boundaries

§ Costs are incurred by farms not parcels

§ More spatial accuracy – Irish Transverse Mercator (ITM)

§ 2.6 million fields, 130,000 farms.

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Issues with OSi 1:50,000 Discovery Series River Data§ Require spatially accurate farm boundary data and

river data§ Ensure a good estimate of the total extent§ Farmers will fence off water courses from the bank

of the watercourse and not from an artificial representation of the centre line

§ The best approach is to identify all the prime2 water polygons and streams that overlap with OSi1:50,000 Discovery Series data

Detailed dataset can be used to examine other

areas: Spatial differences in farm

productivity, biodiversity etc.

Fields bordering water course easily

identified

Spatially Overlay

additional datasets

Nitrates Directive§ The 4th NAP of the EU Nitrates Directive will see the

introduction of a number of measures:• Livestock drinking points to be located at least 20m from waters

(Grassland stocking rate 170 kg N/ ha hereafter referred to as derogation farms)

• Prevention of direct run-off from farm roadways to waters• Prevention of run-off resulting from poaching• Cattle exclusion - a fence shall be placed at least 1.5m from the top of the

riverbank or water’s edge

§ New water protection measures starting1st January 2021. § Watercourse identified on the modern 1:5,000 scale OSi

mapping or better• SI 605/2017 - water course defined• To be defined by DAFM at a later date

Benefits§ It is estimated that cows discharge on average 23,600

grams of faecal matter per 24 hour period (Kenner et al., 1960)

§ Between 6.7% to 10.5% of these cattle defecations occur in stream (Gary et al., 1983)

§ These figures correspond to between 1.6 to 2.5 kg of faecal matter deposited directly in stream per cow daily.

§ A small scale introduction of cattle exclusion measures can significantly reduce nitrogen, phosphorus and sediment run-off from cattle.

§ River bank stability, in-stream vegetation cover and river margin vegetation cover will also improve as a result

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Costs and Benefits§ Costs are represented by cost of erecting a permanent fence

– one strand electric wire, one strand barbed. €3.30 per metre (Teagasc, 2015), lifespan of fence is 5 years. NPV calculated.• Cost figure includes water provision

§ Additional cost of land lost from 1.5m fence.§ Benefits are represented by reduced faecal matter deposits

direct into the river§ !"#$% &$'($% )$##'*+ = 0.067 ∗ !"#$% 23+ ∗ 23.6 ∗ 6ℎ$*' "& 89:'* ;*'$+ ∗ 210

§ On-farm watercourse does not equate with cattle access to river. Current level of access nationally is unknown

§ Probability of cattle access estimated as a function of farm size and length of on-farm watercourse.

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Extent of WatercoursesDerogation

Yes No Total

On-farm Watercourse Yes 9,690 (80%) 84,922 (72%) 94,612 (73%)

No 2,457 (20%) 32,531 (28%) 34,988 (27%)

Total 12,147 117,453 129,600

§ On-farm watercourses more frequent on derogation farms

§ Important considerations for agricultural policy –post dairy quotas

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§ High output areas most cost effective to fence watercourses

§ Clear north/south pattern

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Share costs & benefits

Derogation Share AreaShare Length Share Costs

Share Benefits

Yes 0.16 0.13 0.12 0.23

No 0.84 0.87 0.88 0.77

§ Derogation farms have disproportionate share of benefits

§ Derogation farms represent ~9% of all farms but constitute 23% of all benefits

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Between-within area variability

Between Within Total

Cost Effectiveness 0.07 0.93 1.00

Total Costs 0.07 0.93 1.00

Total Benefits 0.06 0.94 1.00

Length on-farm watercourse 0.06 0.94 1.00

§ I2 index used to decompose variability into between areas component and within area between farms component.

§ Index highlights between area (sub catchment) accounts for 7% of variation in cost effectiveness

§ Within area accounts for 93% highlighting the variation in farm output that exists within each area

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§ Clusters created using agronomic and climate variables

§ These clusters are used to examine how costs and benefits vary depending upon the conditions of the area

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Variable Upland Marginal Low intensity Mid-Intensity High intensity

Continentality 5.2 4.03 5.96 5.89 5.04

Mean temp ('c) 7.94 9.37 9.01 9.14 9.49

Mean precip. (mm) 1617.85 1486.69 1044.49 1052.18 1161.63

Wind speed 7.91 7.64 6.97 7.07 6.96

Moisture 187.79 115.91 25.42 22.34 -3.09

Mean elevation (m) 222.39 72.58 83.5 73.17 93.01

Lu/ha 1.09 1.09 1.05 1.06 1.17

Wet/dry (mode) Poorly Drained Poorly Drained Poorly Drained Well drained Well drained

Soil (mode) Marginal Marginal Marginal Productive Productive

Land use Potential Very Limited Limited Limited Wide Wide

Principal soil (mode) Peaty Podzolic Gley Gley Grey Brown Podzolic Brown Podzolic

Physio (mode) Mountain & Hill Rolling Lowland Drumlin Flat to Undulating Lowland Rolling LowlandPaul Kilgarriff19

Summary Statistics ClustersCluster Costs Benefits Area Length Farms Derogation Farms Cost Effectiveness

Ratio

Upland areas 0.14 0.12 0.09 0.14 0.09 0.02 0.90

Marginal areas 0.17 0.15 0.14 0.17 0.18 0.10 0.90

Low intensity 0.28 0.26 0.26 0.28 0.27 0.20 0.94

Mid intensity 0.25 0.27 0.31 0.25 0.30 0.30 1.09

High intensity 0.16 0.19 0.20 0.16 0.16 0.37 1.15

§ High intensity areas based on area have a disproportionate share of the benefits, highlighting the agricultural intensity in this cluster – 37% of all farms are in derogation

§ Commonage areas excluded from upland areas. Total length underestimated.

§ Low and mid intensity represent the majority of farms and benefits. Their importance should not be ignored. Over 50% of derogation farms also.

§ Clusters highlight the importance of farmer characteristics. Greater variation within area highlights that a farmers characteristics is as important a determinant of agriculture intensity as agronomic and climatic conditions.

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Conclusions§ GIS has capability of enhancing and providing rich datasets

• Spatial analysis can be utilised to identify areas where a policy intervention would have the greatest impact.

§ More variation within area – farmer characteristics and agronomic conditions - The majority of variation in cost effectiveness occurs within areas rather than between• Farm size, length of on-farm watercourse and farmer characteristics play an

important role in determining the level of cost effectiveness for each farm.

§ Targeting agri-environment schemes such as GLAS would increase effectiveness by targeting specific areas. Furthermore farm specific targeting should be explored.

§ More tailored and localised agri-environmental approach• High status and low status areas• Sediment an issue in high status sites, chemical in low status• Spatial analysis can be utilised for this purpose

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Conclusions§ Clusters a useful method of grouping and comparing

similar type farms compared to aggregating using administration boundaries such as counties – Modifiable Areal Unit Problem

§ Fencing areas according to agricultural intensity as recommended in the 4th NAP of the Nitrates Directive is a cost effective solution.

§ Current extent of watercourse fencing on farms unknown; the costs and benefits of cattle exclusion remain relevant.

§ Any scheme which ensures the placement/replacement of a permanent fence along watercourses is worthwhile.

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Thank You

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