沙漠研究 22-1, 45 -48 (2012 )"ICAL 1 / DT X"

Journal of Arid Land Studies－Refereed Paper－

Technical Efficiency of Olive Growing Farms in Tunisia
and Potential Demand for Olive Oil in Japan
Kenichi KASHIWAGI*1), Atsushi KAWACHI1), Sami SAYADI2) and Hiroko ISODA1)
Abstract: In the face of growing international competitiveness, one of the main challenges for olive seeds and olive oil sector in
Tunisia is the enhancement of productivity and the level of technology as well as open up non-traditional, emerging markets such as
Japan. This study investigates firm level technical efficiency of production and its determinants in a sample of olive growing farms in
Tunisia. It also explores the Japanese consumer behavior on olive oil and their potential demand in Japan. Results suggest olive
growing farms in Tunisia can increase their production on an average by 29.8% through more efficient use of technology andproduction inputs. The irrigated olive farms are efficient, and introduction of the irrigation technique and the selection of variety suchas Chétoui and Chemlali significantly contribute to upgrade the technical efficiency. Japanese consumers prefer on extra-virgin andvirgin to olive oil, sweet taste and green color products with the price between 800 to 1000 Yen per 500 ml, and showed higher
preference on olive oil that contains high concentration of polyphenol.
Key Words: Consumer behavior, Japan, Olive growing farms, Technical efficiency, Tunisia

1. Introduction
The olive oil constitutes an important part of the Tunisianexport, accounting for 120,000 tons per year and representing
70% of total production. Tunisia occupies the fourth positionas olive oil exporter preceded by Spain, Italy and Greece;
however most of the olive oil is exported by bulk (99%).
While Tunisian government sought to increase bottled olive oil
exports up to 10% of total exports, this goal has not beenachieved.
On the other hand, olive oil consumption in Japan showed
an increasing trend during the last two decades due to dietaryand health concerns. According to the official statistics, thevolume of import of olive oil in Japan skyrocketed after the
mid-90s. The origin of import was diversified during the 90s
including Greece, France, Portugal, US, Turkey and Tunisia,
however almost 99% of the import was from Spain and Italy(Japan Tariff Association, 2007).
In the face of growing international competitiveness,traditional olive oil producing countries such as Italy, Spain andTunisia may have interest to strengthen their penetration in theJapanese market. For Tunisia as one of the main exporters ofolive oil, the main challenge for the olive oil sector is toenhance the level of productivity and efficiency as well as toexpand export destination to non-traditional emerging markets
such as Japan. Investigation on the technical efficiency ofolive oil production as well as its marketing for designing
preferable product may provide valuable insights into potential

improvement of productivity and export capacity.
A few studies are found in the literature of productivity
analysis on production of olive seeds and olive oil. In Tunisia,
Lachaal et al. (2005) analysed productivity and technicalefficiency of the olive growing farms. Although the majority
investigated the level of technical efficiency itself, few studies
has focused on the effect of non-conventional inputs such as
irrigation and varieties as factors to improve technicalefficiency. Regarding the consumption analysis on olive oil,few studies, except Mtimet et al. (2009), focus on Japaneseolive oil market. While Mtimet et al. (2009) investigated theolive oil consumer behaviour; healthy attribute of olive oil was
not explicitly examined. Abaza et al. (2005) and Yamada etal. (2008) detected biological functions for cosmetic, medical
and medicinal use in the extract of Tunisian olive oil inparticular by applying the bioassay technique.
This study investigates firm level technical efficiency of
production and its determinants in a sample of olive growing
farms in Tunisia. Effects of the introduction of irrigation anddifference in varieties on technical efficiency are explicitly
examined. Also, this study examines Japanese olive oil
consumer behaviour by using the conjoint analysis technique.
Healthy attribute of olive oil indicated by the components ofpolyphenol was introduced in the product design. The
objective is to identify the sources of technical efficiency ofolive growing farmers in the first. Second, the factors
determined the variations of technical efficiency among theolive growing farmers are identified. Third, consumer behavior
on olive oil with different attributes is analyzed in Japan.

* Corresponding Author: kashiwagi.kenichi.fn@u.tsukuba.ac.jp
1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan Tel: +81-29-853-3982 Fax: +81-29-853-5776
1) The Alliance for Research on North Africa (ARENA), University of Tsukuba, Japan

2) Center of Biotechnology of Sfax (CBS), Tunisia

2. Models

if otherwise; wi represents the error term.

2.1. Technical efficiency
In this paper, the Battese and Coelli (1995) model ofstochastic frontier production function is applied in the context
of cross-sectional data. Assuming a Cobb-Douglas form, astandard production function is written as follows:

2.2. Conjoint analysis
The conceptual foundation of conjoint analysis arises from
Lancaster (1966). Krystallis and Ness (2003), empiricallyapplied this framework to the analysis on olive oil
consumption. Following Mtimet et al. (2009), an ordered
probit model has been selected for estimation. The model isbuilt around a latent regression as follows:

Yi = αK iβ k Lβi l M iβ m Oiβ o e vi −ui ,(1)where α = exp(β0); Yi denotes olive production expressed inton; Ki is the number of agricultural machinery (tractors,
cultivators, sprayer) utilized in the production; Li denotes thenumber of days of family and hired labour used; Mi is thevolume of intermediate inputs (costs of fertilizer, pesticides,
ploughing, water and other expenses measured by Tunisan
Dinar) devoted to olive production; Oi denotes the number ofolive trees; βI (i = 0, k, l, m, o) is an unknown parameter to be
estimated; vi refers to statistical random disturbance terms,
assumed to be an independently and identically distributed N(0, σ v2). ui represents non-negative random variables,
assumed to be iid as N (0, σu2) with truncations at zero.
This model is specified in logarithmic form, as in (2).

y i* = λxi + ε i ,(5)where y*i denotes unobserved dependent variable; λ is a vectorof unknown parameters to be estimated; xi is the vector ofindependent variables, which corresponds in this study to theolive oil attributes; εi is the error terms vector assumed to be anormal distribution. As y*i is unobserved, what we doobserve is the ranking variable yi:
yi = 1 if −∞ ≤ y* ≤ µ1, yi = 2 if µ1 ≤ y* ≤ µ2,(6)
yi = 3 if µ2 ≤ y* ≤ µ3, ….. yi = 8 if µ7 ≤ y*,where µj (0 ≤ µ1 < µ2 < … < µj-1) are unknown parameters(thresholds) to be estimated with λ.

ln Yi = β 0 + β k ln K i + β l ln Li + β m ln M i + β o ln Oi + vi − u i , (2)3. DataIn this specification, exp (–ui), varies between 0 and 1, is ameasure of technical efficiency of the ith farm. Following
Battese and Coelli (1995), the technical efficiency of
production of the ith farm is defined by the ratio of theobserved output to the corresponding frontier output:

3.1. Technical efficiency of olive growing farms in Tunisia
The data used in this study were drawn from a survey
conducted in Tunisia in February and Mach 2011. The olive
growing farms surveyed were located in the northerngovernorate (Beja) of the country. The olive growing farms
were randomly selected and the questionnaires were directed tothe owner of olive growing farms. In total, 40 questionnaires
were completed.

TEi = exp(- ui ),0 ≤ TE i ≤ 1.

(3)

To investigate the relationship between technical efficiency
and nonconventional inputs, the technical efficiency of ith farm
can be endogenised as follows:

TE i = δ 0 + δ 1 IRRi + δ 2 EDU i + δ 3TEC i + δ 4 LNDi
(4)
+ δ 5 DCT i + δ 6 DCMi + δ 7 DOFi + wi ,where δ（i= 0…7） is an unknown parameter to be estimated;i
IRR is the share of irrigated olive trees to total; EDU is thediscrete variable that represents the level of education of
employee on a scale of 1 to 5 (1: illiterate, 2: quranic education,
3: primary, 4: secondary, 5: university); TEC is the discretevariable that represents the level of technology on a scale of 1
to 3 (1: traditional, 2: modern, 3: advanced); LND denotes total
area of land devoted to olive cultivation; DCT is the dummy
variable of Chétoui variety that equals 1 if the farm cultivates
this variety and zero if otherwise; DCM is the dummy variableof Chemlali variety that equals 1 if the farm cultivates thisvariety and zero if otherwise; DOF is the dummy of off-farmthat equals 1 if the farm engages in off-farm activities and zero3.2. Olive oil consumer behavior in JapanThe survey was conducted by the NTT Resonant Inc.
which provides internet research services in Japan.
Randomly selected 3127 monitors were questioned through theweb-questionnaire during the last week of March 2011. A
total sample of 532 persons responded to the questionnaire.
The conjoint cards were described by six attributes: countryof origin, price, olive oil type, taste, colour and component(Table 1). The choice of these attributes was based on theanalysis on Tunisian olive oil by Abaza et al. (2005) andYamada et al. (2008). The combination of the six attributes
corresponding to a total number of 384 hypothetical products.
To reduce the number of cards, an orthogonal design procedure(SPSS. 13) is used to lead a final design with 16 products. To
avoid respondents’ fatigue, we blocked the 16 cards into twogroups with 8 cards each.

Table 1. Selected olive oil attributes and their corresponding levels.

Price (500 ml bottle)

Olive oil type
Taste
ColourComponent

Levels
Italy
Spain
Tunisia
Mediterranean
600 Yen800 Yen1000 Yen
1200 Yen
Extra virgin
VirginOlive oil(refined)
BitterSweet
GreenYellow
High polyphenol
Low polyphenol

Table 2. Parameter estimates and t-values of the stochastic frontier
production function of a sample of olive growing farms in
Tunisia. *, **, *** indicate significant at the 10% level, 5%level, 1% level, respectively.
Independent variables
Constant
ln K
ln L
ln M
ln O
Variance parameters
σ2γLog-likelihood

Estimates－6.130 ***0.070 *
－0.1070.207 ***
1.021 ***

t-values－22.272
1.703－0.882
3.211
8.949

***

3.423
16.632

Technical efficiency(%)
0 < TE ≤ 40
40 < TE ≤ 70
70 < TE ≤ 100
Mean efficiency
Min. efficiency
Max. efficiency

Number of Farms

Percentage

3
14
23
70.2
31.6
93.9

7.50
35.00
57.50

Table 4. Parameter estimates and t-values of sources of variation intechnical efficiency of a sample of olive growing farms in
Tunisia. *, **, *** indicate significant at the 10% level, 5%level, 1% level, respectively.
Independent variables
Constant
IRR
EDU
TEC
LND
DCT
DCM
DOF
Adjusted R2
F-statistics
Observations

Estimates
0.460
0.476
－0.0850.071－0.001
0.286
0.357
0.167
0.359
4.13
40

***
***
***
*
***
***
***

t-values
2.973
3.510
－2.311
1.345－1.716
2.385
2.250
2.523

***

1

0.273
0.940
－10.002***

4. Results and Discussion
4.1. Technical efficiency of olive growing farms in Tunisia
Maximum likelihood estimates of the parameters of thestochastic frontier production are obtained using the computer
program FRONTIER 4.1. In Table 2, estimated coefficientsof number of agricultural machinery, intermediate inputs andthe number of olive trees are positive and statisticallysignificant. The negative coefficient of the labour input isinsignificant. The estimate of variance parameter, γ, ispositive and statistically significant at 1% level. Thus, thestochastic frontier function is empirically justified.
In Table 3, the average level of technical efficiency is
70.2%, ranging from a minimum of 31.6% to a maximum of
93.9%. Twenty three farms represented 57.5% in the sample
are more efficient than the average; however 7.5% of thesample shows relatively inefficient with the scores showed lessthan 40%. These results suggest the possibility of enhancing
efficiency for upgrading the productivity and competitiveness.
The OLS estimation results to investigate the relationshipbetween technical efficiency and nonconventional inputs arepresented in Table 4. The estimated coefficient of the rate ofirrigated olive trees is positive and statistically significant.
This result clearly indicates that the irrigation of olive trees
contributes to increases in technical efficiency. The positive
sign of the dummy variable of Chétoui (DCT) and Chemlali

0.9
0.8

Technical efficiency (%)

Attributes
Region of origin

Table 3. Frequency distribution of technical efficiency for a sampleof olive growing farms in Tunisia.

0.7
0.6
0.5
0.4
0.3
T E in non-irrigated olive farms

0.2

T E in irrigated olive farms

0.1
0
0

10
5
15
Production per olive tree (kg)20

Fig. 1. The relation between productivity and technical efficiency ofa sample of olive growing farms in Tunisia.

(DCM) confirm the positive effect of cultivation of thesevarieties on the technical efficiency. The positive sign of theestimated coefficient of the off-farm dummy (DOF) indicatesthe involvement with non-agricultural activities has positiveeffect on technical efficiency. The negative sign of area ofolive cultivation (LND) indicates the efficiency tends to decline
as the area become large.
Figure 1 shows the relation between the level of technicalefficiency and productivity. While the level of technicalefficiency and productivity vary across farms, it is clear that theirrigated olive farms are efficient. Comparing the mostefficient group between the irrigated and the non-irrigated, theproduction per olive tree of the irrigated is higher than
non-irrigated.
This result implies that the irrigation
contributes to further enhance the productivity.

Table 5. Parameter estimates and standard errors of the ordered probit
model of a sample Japanese consumers. *, **, *** indicate
significant at the 10% level, 5% level, 1% level, respectively.
Model 1Model 2
IndependentStandard
Standard
Estimates
Estimates
variables
error
error
***
*
Constant-0.941
0.067-0.6740.344
Type
Virgin0.484 ***
0.043
0.480 ***
0.043
Extra virgin
1.104 ***
0.056
1.088 *
0.554
Origin
Italy
0.027
0.049
0.029
0.049
Spain
0.001
0.050
0.002
0.049
*
*
Tunisia-0.0930.051-0.0950.051
Taste: bitter-0.283 ***0.036-0.285 ***0.036
Colour: green
0.192 ***
0.036
0.188 ***
0.036
Component:
***
***
high
0.465
0.036
0.463
0.036
polyphenol
Price
1.048
0.802
Price2-0.001 **0.000
800 JPY
0.046
0.049
1000 JPY-0.223 ***0.050
1200 JPY-0.318 ***
0.051

µ2
µ3
µ4
µ5
µ6
µ7

Observations
LR1 χ2(11)LR2 χ2(10)-0.404
0.005
0.372
0.740
1.149
1.692
3504
873.2

***
***
***
***
***
***

0.065
0.065
0.065
0.066
0.067
0.069

-0.139
0.269
0.636
1.003
1.412
1.954
3504
863.5

*
***
***
***

0.344
0.344
0.344
0.344
0.344
0.344

selection of variety such as Chétoui and Chemlali significantly
contribute to upgrade technical efficiency. Regarding theresults of conjoint analysis, Japanese consumers prefer on
extra-virgin and virgin to olive oil, sweet taste and green color
products with the price between 800 to 1000 Yen per 500 ml.
While the preference for the country of origin indicated at
Tunisia is less compare with the label of Mediterranean, theconsumers showed higher preference on olive oil that contains
high concentration of polyphenol. The olive oil made of thevariety of Chétoui that contains plenty of polyphenol would
have a potential to be exported to this emerging market.

Acknowledgement
The authors gratefully acknowledged that this research was
supported by the JICA-JST Science and Technology ResearchPartnership for Sustainable Development Project: “Valorizationof Bio-resources in Semi-Arid and Arid Land for Regional
Development”, and supported by the JSPS Asia Africa Science
Platform Program: “Establishment of Integrative ResearchBase by Humanities and Sciences on Valorization of Useful
Plants for Regional Development in North Africa”.

***

4.2. Olive oil consumer behavior in Japan
Parameter estimates and standard errors of the ordered
probit model are given in Table 5. The estimated coefficientsof the both models showed clear results.
First, therespondents prefer virgin and extra virgin olive oil to olive oil(refined). Second, respondents prefer a sweet taste olive oilrather than a bitter one. Third, respondents show also ahigher preference for a green coloured olive oil compared witha yellow one. It is noteworthy that the positive andstatistically significant estimation of the coefficient of the highpolyphenol indicates respondents prefer olive oil that contains
higher concentration of polyphenol.
The coefficients
correspond to the Italy and Spain origins are insignificant whilethat of Tunisia presents the negative sign.
Finally,
respondents’ preference tends to increase when price increases
from 600 and 800 Yen, but it decreases when price increases atthe level more than 1000 Yen.

5. Conclusion
Results of the estimation of stochastic frontier production
suggest that olive growing farms in Tunisia can increase theirproduction on an average by 29.8% through more efficient useof technology and production inputs. The irrigated olive
farms are most efficient than non-irrigated farms. It is also
implied that the introduction of the irrigation technique and theReferences
Abaza L., Taamalli W., Ben Temime S., Daoud D., Gutierrez.
F., Zarrouk M. (2005): Natural Antioxidant Composition as
Correlated to Stability of Some Tunisian Virgin Olive Oils.
Rivista Italiana delle Sostanze Grasse, 82: 12-18.
Battese G.E., Coelli T.J. (1995): A Model for TechnicalInefficiency Effects in a Stochastic Frontier Production
Function for Panel Data. Empirical Economics, 20: 325-332.
Japan Tariff Association (2007): Japan Export & Imports:
Commodity by Country, 2006, March, Japan Tariff
Association: Tokyo.
Krystallis A., Ness M. (2003): Motivational and Cognitive
Structures of Greek Consumers in the Purchase of Quality
Food Products. Journal of International Consumer
Marketing, 16(2): 7-36.
Lachaal L., Karray B., Dhehibi B., Chebil A. (2005): Technical
Efficiency Measures and Its Determinants for Olive
Producing Farms in Tunisia: A Stochastic Frontier Analysis.
African Development Review. 17(3): 580-591.
Lancaster K. (1966): A New Approach to Consumer Theory.
Journal of Political Economy, 74(2): 132-157.
Mtimet, N., Kashiwagi K., Zaibet L., Nagaki M. (2009):
Japanese Olive Oil Consumer Behavior: A Conjoint
Analysis Approach. In Sato eds., Consumer Behaviour.
Nova Science Publishers, Inc., New York, 27-47.
Yamada P., Zarrouk M., Kawasaki K., Isoda H.(2008):
Inhibitory Effect of Various Tunisian Olive Oils on ChemicalMediator Release and Cytokine Production by Basophilic
Cells. Journal of Ethnopharmacology, 116, 279-287.