Impact analysis of the EU decoupling policy with agricultural-geographicalinformation: An application to the LFAs in Northern England
The University of Tokyo, Hironori Yagi*
Newcastle University, Guy Garrod
1. Introduction
Since 2005, England has endorsed a relatively strict decoupling policy according to which theSingle Farm Payments schemes will be linked with land area instead of the number of livestock, i.e.,cattle or sheep. Numerous papers have reported on the impact of this policy; Our paper aims tocontribute to the existing body of analyses in three distinct ways: (1) to provide regional level
econometric analysis for the Less Favoured Areas (LFAs) with a combination of agricultural statistics
and geographical information, (2) to adopt an empirical approach for multi-output production withcattle and sheep, (3) to predict the integrated impacts of decoupling and the agri-environmental
schemes (AES).
Several literatures have studied the changes in LFAs caused by the implementation of thedecoupling policy. Some of these documents employed national level statistics1, whereas others
conducted mail-in surveys such as the ‘farmers’ voice survey’2. However, there have been relatively
few papers to have provided empirical evidence of the policy’s impact on the regional level. The term
‘regional level’ in this context refers to means a spatial range that includes several local county
authorities and has relatively homogeneous socio-economic and technical characteristics for
agricultural production.
In this paper, Farm Business Survey (FBS) farm level data from Northern England, where LFA
cattle and sheep production dominates its agriculture, are aggregated on a GIS platform in order toprepare for the regional level analysis. Then, using aggregated spatial units, empirical econometricand simulation models are elaborated in order to predict the combined impacts of decoupling and AES.
Data for the model and its results are then reported. The paper concludes with a discussion section.
2. Aggregation of regional agricultural statistics and geographical information
We begin with an introduction of a data processing procedure that is applied in Northern England.
The FBS data stores a National Grid (10 km × 10 km grid) reference for each farm as geographicalinformation, while socio-econometric characteristics are considered on the basis of local authorities’
boundaries such as Nomenclature of Units for Territorial Statistics (NUTS). The results of the FBS
are not available to the public due to the confidential nature of the information unless the aggregateddata contains six or more individual farms. Hence, a systematic aggregation procedure is needed toutilise FBS for a regional level analysis.
Although it may have been possible to build a sophisticated mathematical model, such as one withcombination theories or other operations research models, we adopt a primitive overlay method (Fig.1).
This simplification is justified because our data contains approximately one hundred samples at most,
1 Moss et al. (2002),

Revell and Oglethorpe (2003)，Hall et al. (ADAS)( 2002)

2 Seabrook, M. and Darling (2006), Temple et al. (ADAS) (2005)

which is a fairly small number and which permits step-by-step decision-making. Furthermore,because the area of study falls within only two Regional Government boundaries, Northeast andNorthwest England, decent geographical knowledge is assured.
Criteria for the aggregation are as follows.
1) The most important criterion is that the number of FBS sample farms in the aggregated gridshould be six or more.
2) The consistency and continuity for time series should be maintained.
3) Each group should be geographically adjacent and contiguous.
4) Each group boundary should be approximate for the MSOA government boundaries
5) Each group should be homogeneous in terms of Agricultural Land Classification
The method produces eleven spatial groups of aggregated cells are produced (Fig.2). The bold
numbers in Fig.2 (one to eleven) represent cells for each group. County boundaries are illustrated byblack winding lines and are considerably well coordinated with each aggregated group.

FBS

National Grid
（10 km ×10 km）
Agricultural land
classification

June Agricultural
Census (2004)

MSOA boundary

Ward (1998）
boundary

June Agricultural
Census(1998–2003)

11 aggregated groups（Multiple cells based on 10km×10km unit cells）

Fig.2 Aggregated Groups in Northern EnglandFig. 1 Procedure of the Overlay
3．Empirical analysis of the decoupling
3.1. Estimation ModelA Cobb-Douglas (CD) type production function has been adopted for the estimation model because
the self-duality of the function form is convenient when calculating shadow prices from the derivationof production function despite its inflexibility.
Each production function for livestock i, i = 1, 2 is25

Yi = X i ⋅ ∑ Z k ⋅ exp(∑ d it Dt + α i )
βi

k =1

γi

(1)

t =1

where Yi is the output quantity; Xi is the number of livestock in the farm; Zk is the land area for land
use type k, k = 1, 2; Dit denote the dummy variables for year t, t = 1…5; and βi, γi, dit, and αi are
the parameters to be estimated.
Each farm receives both headage and AES compensational payments. If AES is delivered on thebasis of farmland area, the profit function can be written as follows:
2

2

2

i =1

i =1

i =1

π = ∑ piYi + ∑ hi X i + g ( Z1 , Z 2 ) − ∑ λi X i(2.a)

where π represents the total gross margin, hi denotes headage payments per livestock, g(・) is afunction of the AES compensational payments and λi is the shadow price for grazing one suckler cow
or breeding ewe which can be tested by the total variable cost.
Equation (2.b) represents a similar case but the amount of AES here is a function of the livestock
density l. Equation (3) defines livestock density l, where one suckler cow equals 0.15 breeding ewe
based on livestock unit (LU).
2

2

2

i =1

i =1

i =1

π = ∑ piYi + ∑ hi X i + g (l ) − ∑ λi X i(2.b)，l =X 1 + 0 . 15 X 2

∑Z

(3)

k

k

3.2. Simulation Modelλi for each sample farm can be derived from the first order condition of profit equation (2.a) or (2.b).
In the case of (2.b), the profit maximizing condition is:p ⋅ βˆ ⋅ Y∂π= i i i + hi + a i = λi
∂X i
Xi
,

a1 =

ŝ

∑Z

,

a2 =

k

k

where s is an estimated linear gradient of g(l).

0.15sˆ

∑Z

(4)
k

k

Equations for a1,a2 can be derived from (3) bydefinition.
Substituting (1) into (4) gives5ˆ
pi ⋅ βˆi ⋅ X iβi −1 ⋅ Z iγˆ ⋅ exp(∑ dˆt Dt + αˆ i ) + hi + ai − λi = 0
t =1(5.1).

Solving (5.1) for Xi gives

Xi = ˆ

β i −1λi − hi − ai5pi ⋅ βˆi ⋅ Z iγˆ ⋅ exp(∑ dˆt Dt + αˆ i )t =1(5.2).

Equation (5.2) can be used to simulate the situation under given levels of headage payments hi.
When hi is zero, the subsidy is decoupled in terms of headage payments although the AES components
should be considered, which is normally ai <0.
4. Data
Table 1 summarises the variables for the estimation models. The data contains 66 samples of
FBS compiled over six years (1999–2004), which are aggregated into eleven cross-section groups in
Section 2.

Table 1: Summary of Variables
Variables

Average

Dependent
Y1
The output number of calves
Y2
The output number of lambs
Independent
X1
The number of cattle in the farm
X2
The number of sheep in the farm
Z1
Area of grassland (ha)Z2Area of rough grazing (ha)DitYear dummy (each year: 1)
Dependent
ENV
Environmental compensational payments (in pounds)
IndependentlLivestock density
DEt
Year dummy (each year:1）
Source：FBS data aggregation by authors

S.D.

80.3
521.8

29.2
277.0

51.2
575.8
86.7
154.0

15.1
283.6
24.0
143.2

―
21,400―9,265.1

0.69
―

0.23
―

5. Results
5.1. Estimation Results of Production Functions and Environmental Payments
Table 2 indicates the results of estimation. The livestock input quantities for the cattle CDproduction function seem significant; however, the goodness of fit of the model is not as high as
expected. On the other hand, the sheep CD production function is well behaved except that input
area of rough grazing turns out to be a negative coefficient. To avoid heteroscedasticity, every
variable in the environmental payment function is multiplied by the inverse of l. The goodness of fitof 0.629 is considered to be relatively suitable.
Table 2: Results of Estimation (OLS)

Independent Variables
X1 cattle
X2 sheepl livestock density
Z1 grassland
Z2 rough grazing
Year dummy 1999
2000
2001
2002
2003
Constant terms
Adj.R2

CD Production Function(Cattle)Coefficients.
t
0.511
2.74 **
0.270
1.23-0.189 -2.07 *-0.324 -3.32 **-0.151 -1.35
1.251
2.36 *
0.403

CD Production Function(Sheep)Coefficientst
0.726
7.39 **
0.542
4.63 **-0.054 -1.52-0.714 -8.76 **-0.429 -5.28 **-0.268 -2.79 **-0.318 -0.65
0.842

Environmental Payment
Coefficientst-10493 -1.89 +-13237 -3.72 **-4805 -1.44-3276 -1.04
32409 10.13 **0.629

Note: **, * and + denote significance at the 1%, 5%, and 10% levels, respectively.

5.2 Parameters for the simulation
As a result of the estimation in the previous section, the parameters for the simulation model aswell as several exogenous parameters can be prepared (Table 3). According to a comparison between
λ calculated from equation (4) and the total variable cost, the authors chose model (2.b) rather than
(2.a). Output prices p1 and p2 are those of previous years from DEFRA’s ‘Index of Producer Prices ofAgricultural Products’. h1 and h2 are from the same FBS dataset.

For the simulation below, three cases of farm size have been prepared. The ‘Average’ casescenario represents the 2004 mean values. The ‘Small’ and ‘Large’ cases scenarios demonstratesimulations that are equivalent to those ofprevious literatures (Hall et al. (ADAS)(2002)).

The

three

situationsaresimulated here: (1) without headage
payments: both h1 and h2 are zero; (2)without environmental payments: not only
h1 and h2, but also a1 and a2 are zero; (3)Intensive Farm Promotion: s = 2,000
instead of the negative value estimated in
the previous section.

Table 3: Parameters in the Simulation Model
Average Small Largea1-45.3 -104.9 -19.1
ｓ/(Z1+Z2)
a2-6.79 -15.74 -2.86
ｓ*0.15/（Z1+Z2）
Z1 grassland
88
70
150
Z2 rough grazing 14330400
ｓ-10,493s=2,000 in situation(3)
λ1
518.8
λ2
50.1
h1
267.8
Cattle headage payments
h2
4.3
Sheep headage payments
341.3
Cattle output pricep1
66.1
Sheep output pricep2
α，β，γ
See Table 2

5.3 The result of simulation after decoupling
Fig. 3 illustrates the resultof the simulation for the total
gross margin (TGM).

Since

TGM is the total income before
deducting the fixed cost, theaverage fixed cost in 2004 is
drawn for reference at the top of
all the bars.
Inthescenario

without

headage payments, the TGM is
likely to increase by 10% in the
averagecasebecausetheFig.3: Simulation Results for Total Gross Margin

reduction of direct payments can be covered by the AES associated with the reduction of livestock
densities. However, without environmental payments, the average case TGM will decrease by 35%.
Another point to be noted is that without headage payments, TGM gaps between small and large
farms will increase. This is mainly due to the SFP installation that was based only on farmland area.
For example, in the scenario without headage payments, large farms will receive 13% more TGM
than they currently do while small ones will gain only 7% more. Alternatively, if intensive farms arepromoted by the AES, small farms will gain 26% more TGM, while larger farms will only gain 20%
more TGM.
In Fig.4, the simulation result for livestock density for each case and scenario is reported.

The

predicted livestock density for the average case without headage payments is decreased by -42% ascompared to the current situation, which consists of a -73% and -25% declines of cattle and sheep,
respectively. This decrease is due to the combined impacts of decoupling and the AES. If there is noenvironmental payment available in the future, livestock density decrease for the same case is -12%.
In the scenario of intensive farm promotion, livestock density for the average case decreases by -4%.

For smaller farms, there seems to be incentives to increase the number of sheep in the latter two
scenarios.
6. Conclusion
In this paper, the combined impact of decoupling and AES are predicted econometrically with theuse of spatially aggregated FBS data. Table 4 compares the results of previous literatures with thesimulation results of this paper. Our prediction for farm outputs is relatively positive reflecting theavailable AES. Although, the ‘farmer’s voice’ islikely to be conservative in terms of reducing
livestock inputs, optimal livestock density seems
to be further less than the current situation after
decoupling.

This is partly because the AES

accelerates the incentives for reducing livestock
as well as decoupling.
Our simulation result also suggests that the
combination of decoupling and AES tends toincrease income gaps between large scale farms
and smaller ones because the SFP is based on
farmland area and AES encourages extensive
farms. If AES promotes intensive farming thesegaps can be decreased.

Fig.4: Simulation Results for Livestock Density

Table 4: Comparisons with Previous Literatures for the Impact of Decoupling
SourceThis paper
Moss et al.
(2002)

OutputTGM +10%

Livestock inputs
MethodLivestock density: -42%
CD production function for Northern(cattle:-73%, sheep:-25%)
England with FBS (1999-2004)
Market receipts for the beef The number of suckler cows: Partial equilibrium model for EU
sector (UK): +4.3%,-18.9% and that of ewes: -10.6%
countries
sheep sector: +4.7%
Mathematicalprogrammingfor
Net farm income (England
LFA cattle & sheep): -2%
different farm types

Revell andOglethorpe(2003)
Hall
etal. Business profit for medium The number of livestock : -80%~
Representative farm models with FBS(ADAS) ( 2002) size SDA cattle & sheep(1999,2000) data
farms: +30.1%
The number of suckler cows:-35%, ‘The Farmer’s Voice 2004 Survey’
Temple et al.
that of breeding ewes:-3% and the (approximately 40 LFA cattle and sheep(ADAS)(2005)
livestock density: -2% (LFA)
farm respondents)

References
Hall, W.A., Phillips, K.A., Ford, S.A., Rockliffe, J., Woodcock, D. and Topliff, M., Assessment of the Potential Impact on
Beef and Sheep Farming of CAP Aid Payments from Production，Report to DEFRA，ADAS, 2002.
Moss, J., McErlean, S., Kostov, P., Patton, M., Westhoff, P. and Binfield, J., Analysis of the Impact of Decoupling on
Agriculture in the UK, Report to DEFRA，Queen’s University Belfast and University of Missouri, 2002.
Revell, B. and Oglethorpe, D.R., Decoupling and UK agriculture: A whole farm approach. Report to DEFRA, Harper Adams University
College and SAC Edinburgh, 2003.
Seabrook, M. and Darling, R., “Farmer intentions in response to the Single Farm Payment”, Journal of Rural Enterprise and Management,
Vol.2, No.1, 2006, pp.21-35.
Temple, M., Hollis, K., McIntosh, L. and Augustin, B., The Economics of Extensive Livestock Grazing Post CAP Reform–Analysis of ADAS Farmer’s Voice 2004 Survey, ADAS, 2005.