Level of intensification and farmers’ preferences
towards agri-environmental schemes: The case of olive groves in Southern Spain
A.J. Villanueva1,2, J.A. Gómez-Limón1, M. Arriaza1,2, M. Rodríguez- Entrena2
1 University
of Cordoba (UCO), Dept. of Agricultural Economics, Córdoba
(Spain).
2 Institute of Agricultural and Fisheries Research and Training (IFAPA),
Centre Alameda del Obispo, Córdoba (Spain).
Provide Workshop “Economics and policy of the provision of public
goods by agriculture and forestry: state of the art and challenges”
Accademia delle Scienze, University of Bologna, 23rd Sept. 2015.
1. INTRODUCTION
1.1. The design of agri-environmental schemes
(AES)
➢ Extensive literature
➢ Level of intensification and AES
➢
uptake: little attention
Scarce literature: focused on systems
with different crops/livestock (e.g.
Ducos et al., 2009; Hynes and Garvey, 2009)
➢ Within the same agricultural system,
level of intensification seems to
matter (Wynn et al., 2001; Defrancesco et
al., 2008)
Higher level of intensification ! Lower AES
uptake
1. INTRODUCTION
1.2. Objective
➢ To analyse the influence of the level
of intensification of agricultural
sub-systems on farmers’ preferences
towards AES.
2. CASE STUDY
Olive grove in Andalusia: 3 main subsystems
Mountainous
olive groves
(MOG)
▪ Steep slope
▪ Soil quality:
low
▪ Yield: low
–
Rainfed
tradic. olive
groves (ROG)
▪ Low to
Irrigated
tradic. olive
groves (IOG)
▪ Low to moderate
moderate slope slope
▪ Soil quality: ▪ Soil quality:
medium-high
medium-high
▪ Yield: medium ▪ Yield: mediumhigh
Intensification
+
2. CASE STUDY
Mountainous
olive groves
(MOG)
Rainfed tradic.
olive groves
(ROG)
Irrigated
tradic. olive
groves (IOG)
3. METHOD
3.1. Choice experiments (CEs)
➢ Attributes and levels
Attribute (acronym)
Levels
Cover crops area (CCAR)
25%, 50%
Cover crops management
Free, Restrictive
(CCMA)
Ecological focus areas
0%, 2%
(EFA)
Collective participation
Yes, No
(COLLE)
Monitoring (MONI)
5%, 20%
Yearly payment per hectare 100, 200, 300, 400
(PAYM)
(€/ha)
Example of
choice card
▪ Choice
between A, B
and C
▪ C represents
SQ or no
participation
▪ Previous
information
3. METHOD
3.2. Experimental design and survey
➢ Orthogonal optimal design (OOD) (Street and
Burgess, 2007) ! 192 choice cards (D-efficiency=91.3%)
➢ 1 farmer – 8 choice cards
➢ Sample: 330 face-to-face interviews (293 valid)
(Oct 2013 – Jan 2014)
▪ 75 MOG
▪ 116 ROG
▪ 102 IOG
3. METHOD
3.3. Econometric specification
➢ Utility function
�
𝑈�
𝛼�𝑛�
𝑝�
𝛽�𝑛�
′𝑥�
𝜗�
𝜀�
�
𝑛�
𝑖𝑡 = �
�
𝑛�
𝑖𝑡 + �
�
𝑛�
𝑖𝑡 + �
�
𝑛�
𝑖𝑡 +�
�
𝑛�
𝑖𝑡
′
where p and x are monetary and non-monetary
attributes of the experimental design,
α and β are parameters to be estimated,
ε is the random error term, which is assumed to be
identically and independently distributed (iid) and
related to the choice probability with a Gumbel
distributed error term,
the additional error component ϑnit (distributed with
N(0,σ2) was included in the utility function, capturing
the error variance shared by both A and B.
3. METHOD
3.3. Econometric specification
➢ Random parameter logit model with
error component (EC_RPL)
Probability of respondent’s n to choose the
alternative i in each of the T choice tasks
4. RESULTS
➢ Description of the olive grove sub-systems.
Average values of numerical variables
Variable
MOG
ROG
IOG
Mean St.dv. Mean St.dv. Mean St.dv.
Olive tree area (ha)
23.3 31.2 34.7 64.4 33.9 67.8
Total area (ha)
24.1 32.2 48.4 96.0 37.6 69.2
Tree density (trees/ha)
122.7 44.7 119.7 57.4 138.0 72.5
Slope (%)
21.7 b 8.8
4.6 a 3.7
5.2 a 4.2
Family labour over total farm labour [Famlabour] (%)
67.2 31.0 63.4 30.9 62.8 29.9
3
Irrigation water (m /ha)
0a
0
0a
0 923 b 589
Yield (kg/ha)
2615 a 1596 4416 b 1571 6337 c 2164
CC / olive tree area (%)
42.6 b 27.7 17.0 a 17.8 21.6 a 18.4
EFA / olive tree area (%)
3.2 b 4.0
0.5 a 1.1
0.7 a 1.5
Ground harvested / total olive harvested [Groundharv] (%)
12.4 a 19.1 11.8 a 18.0 23.8 b 28.5
Age [Age] (years)
52.9 12.9 50.3 12.6 48.5 10.0
Farmers’ working time on the farm (% of total working time) 46.1 40.8 50.5 39.7 56.3 38.5
Perception of CC as profitable [PerCCprofit] (1-5)
3.64 a 1.41 3.16 a 1.47 3.61 a 1.44
Perception of EFA as environmentally beneficial
[PerEFAbenef] (1-5)
4.03 b 1.30 3.65 a 1.29 3.84 ab 1.21
KruskalWallis
H p-value
1.41 0.494
2.00 0.368
2.01 0.367
164.14 0.000
1.36 0.506
273.35 0.000
122.87 0.000
43.90 0.000
53.20 0.000
8.84 0.012
5.14 0.077
4.35 0.114
7.35 0.025
6.07 0.048
4. RESULTS
MOG
Coef.
Attributes and constant
Cover crops area (CCAR)
Cover crops mgmt. (CCMA)
Ecological focus areas (EFA)
Collective part. (COLLE)
Monitoring (MONI)
Payment (PAYM)
ASCSQ
St. deviations
CCAR
CCMA
EFA
COLLE
Latent random effects
McFadden Pseudo-R2
*, **,
St. e.
ROG
Coef.
St. e.
IOG
Coef.
St. e.
-0.126 *** 0.017
-1.626 *** 0.433
-0.574 *** 0.132
-2.206 *** 0.550
0.014 0.016
0.014 *** 0.002
0.272 0.684
-0.082 *** 0.014
-4.357 *** 0.489
-0.801 *** 0.132
-2.535 *** 0.408
-0.033 * 0.016
0.013 *** 0.001
-1.179 * 0.530
-0.103 *** 0.015
-1.344 *** 0.300
-0.978 *** 0.168
-1.556 *** 0.331
-0.008 0.012
0.013 *** 0.001
-1.014 0.577
0.104 *** 0.024
3.018 *** 0.577
1.384 *** 0.221
2.645 *** 0.531
3.168 *** 0.477
0.427
0.103 *** 0.014
4.279 *** 0.729
0.676 ** 0.226
4.437 *** 0.549
3.256 *** 0.379
0.456
0.132 *** 0.019
1.903 *** 0.309
1.145 *** 0.177
1.898 *** 0.333
3.262 *** 0.404
0.428
y *** represent significantly different from zero at 95%, 99% and 99.9%
levels.
4. RESULTS
➢ Willingness to accept (WTA) for each
attribute (in €/ha·year), for the three subsystems
Attributes
MOG
ROG
IOG
Cover crops area (CCAR)
8.8 ***,a
6.5 ***,a
7.7 ***,a
Cover crops mgmt. (CCMA) 112.4 ***,a 341.3 ***,b 101.1 ***,a
Ecological focus areas (EFA) 39.2 ***,a 63.2 ***,b 72.4 ***,b
Collective part. (COLLE)
153.6 ***,ab 197.5 ***,b 117.2 ***,a
Monitoring (MONI)
-0.9 a
2.6 *,a
0.7 a
*, **,
y *** represent significantly different from zero at 95%, 99% and 99.9% levels, using
the test proposed by Poe et al. (2005).
4. RESULTS
➢ CCAR, CCMA, COLLE and MONI do not show the expected relationship between the level
of intensification
and farmers’ preferences
The
level of intensification
seems to
towards
have
an AES
influence on farmers’ WTAs, with
➢ tCCAR
and
show
he m
a gCCMA
nitud
e odifferent
f t h i s joint
influence
production on the attribute, while other
depending
at SQ: MOG
43%;different
ROG 17%; IOG
22%
à Mean CCAR
factors
(especially
the
joint
à CCMA: ROG more difficult mgmt. and greater
production)
appear
to also
fears about the
competition
(CC vs.influence
tree) for
farmers’
water preferences.
➢ Influence on COLLE and MONI
4. RESULTS
➢ Total WTA per AES-scenario (in €/ha·year)
for the three sub-systems
Scenario
Maximum AES Individual
Maximum AES Collective
MOG ROG IOG
215.0 645.5 409.4
368.9 843.0 526.6
➢ Level of intensification: more
extensive sub-system higher
willingness to participate but …
5. CONCLUSIONS
➢ In general, our results are in keeping with
previous findings in the literature showing
that farmers are more willing to participate
in AES when they have less intensive farms
(e.g. MOG)
➢ However, results also suggest that the level
of intensification does not completely
determine farmers’ willingness to
participate in AES. So, the most intensive
sub-system (IOG) shows a higher willingness
to participate than the intermediateintensive sub-system (ROG).
5. CONCLUSIONS
➢ In fact, there are certain specificities of
each sub-system’s join production that
also determine such participation. This is
clear by comparing the welfare estimates
for not only the attributes but also the AES
scenarios.
➢ Results suggest that both the level of
intensification and the specificities of the
joint production must be taken into
account for the efficient design of AES.
REFERENCES
Defrancesco E, Gatto P, Runge F, Trestini S. 2008. Factors affecting
farmers’ participation in agri-environmental measures: A northern
Italian perspective. J. Agr. Econ. 59(1):114-131.
Ducos G, Dupraz P, Bonnieux F. 2009. Agri-environment contract
adoption under fixed and variable compliance costs. J. Environ.
Plann. Man. 52(5):669-687.
Hynes S, Garvey E. 2009. Modelling farmers’ participation in an agrienvironmental scheme using panel data: An application to the rural
environment protection scheme in Ireland. J. Agr. Econ. 60(3):
546-562.
Poe GL, Giraud KL, Loomis JB. 2005. Computational methods for
measuring the difference of empirical distributions. Am. J. Agr. Econ.,
87(2):353-365.
Street DJ, Burgess L. 2007. The construction of optimal stated choice
experiments: theory and methods. John Wiley & Sons, Hoboken (New
Jersey).
Wynn G, Crabtree B, Potts J. 2001. Modelling farmer entry into the
ACKNOWLEDGEMENTS
This research has been financed by the Regional
Government of Andalusia (Junta de Andalucía) through
the research projects P10-AGR-5892 (SUSTANOLEA) and
AGL2013-48080-C2-1-R (MERCAGUA). Also, by the
research project RTA2013-00032-00-00 (MERCAOLI) cofinanced by the National Institute of Agricultural
Research (INIA) and Ministerio de Economía y
Competitividad (MINECO) as well as the European Union
through the European Regional Development Fund
(ERDF) 2014-2020 “Programa Operativo de Crecimiento
Inteligente”. The first and the last authors acknowledge
the support provided by the Andalusian Institute of
Agricultural Research and Training (IFAPA) and the
European Social Fund (ESF) within the Operative
Program of Andalusia 2007–2013 through postdoctoral
Thank you very much for your attention!!!
Level of intensification and farmers’ preferences
towards agri-environmental schemes: The case of olive groves in Southern Spain
A.J. Villanueva1,2, J.A. Gómez-Limón1, M. Arriaza1,2, M. Rodríguez- Entrena2
1 University
of Cordoba (UCO), Dept. of Agricultural Economics, Córdoba
(Spain).
2 Institute of Agricultural and Fisheries Research and Training (IFAPA),
Centre Alameda del Obispo, Córdoba (Spain).
Provide Workshop “Economics and policy of the provision of public
goods by agriculture and forestry: state of the art and challenges”
Accademia delle Scienze, University of Bologna, 23rd Sept. 2015.
1. INTRODUCTION
1.1. Ecological focus areas (EFA)
➢ Greening, EFA:
▪ Areas with landscape features, terraces,
buffer strips, land lying fallow, afforested
areas and agro-forestry areas, or farmland
employing reduced farming inputs
▪ CE 2011’s proposal ! 7% EFA in
permanent crops
▪ R (EU) 1707/2013 ! 0% EFA in permanent
crops
Future CAP reforms? Probably a X% EFA in permanent
crops