Eciency of government-supported horticulture:
the case of Oman
L. Zaibet
a, P.S. Dharmapala
b,*
aCollege of Agriculture, Sultan Qaboos University, Oman bCollege of Commerce and Economics, Sultan Qaboos University, OmanReceived 4 December 1998; received in revised form 24 June 1999; accepted 17 September 1999
Abstract
This paper analyzes technical eciency in Oman using the stochastic production frontier and the data envelopment analysis (DEA) methods. Dierent methods are used because the determinants of technical eciency may be in¯uenced by the method used and also by the assumptions (such as returns to scale) maintained. Results from the stochastic parametric frontier (SPF) and DEA±Charnes, Cooper, Rhodes (CCR) models show that the percentage of farmers that could qualify as technically ecient is as low as 17%. When the DEA±Banker, Charnes, Cooper (BCC) method was used, this percentage increased to about 46%. Factors such as o-farm income and soil quality were found to be positively correlated to productivity. On the other hand, small farm size and farmer's age showed a negative relation-ship with productivity.#2000 Elsevier Science Ltd. All rights reserved.
Keywords:Technical eciency; Stochastic production frontier; Data envelopment analysis; Agriculture; Oman
1. Introduction
In the Arab Gulf countries, agriculture has enjoyed a substantial output growth due to sig-ni®cant government support. In the Sultanate of Oman, the area under cultivation has moved from 41,000 ha in 1987 to 71,000 ha in 1994, an increase of 73%, and output has increased by 43% during the same period (CBO, 1995). A range of policies has been implemented aiming at creating employ-ment for the national workforce, diversifying the sources of revenues and achieving a sucient level of food security. Support programs included direct free services, heavily subsidized inputs, and free
and easy credit guaranteed above market prices for some products (Mahdi, 1996). Moreover, the government of Oman has imposed import restric-tions to protect local producers (JICA, 1990) and supported marketing facilities such as the Public Authority for Marketing and Produce (PAMAP) to help farmers to market their products more eciently.
A major support program, and maybe the most signi®cant of all support programs in the oil-exporting Arab Gulf countries, in general, comes from the current land policies and unrestricted access to groundwater resources (Mahdi, 1996). Substantial drilling of wells was carried out after the oil boom to expand agricultural lands and boost agricultural production beyond the tradi-tional small farms (Al-Kuwari, 1996). In Oman, approximately half of the irrigated area utilizes
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* Corresponding author.
wells and pumps to extract groundwater (Abdel-Rahman and Abdel-Majid, 1993). During the last two decades traditional wells have been replaced by motorized pumps and virtually all wells in the coastal belt were mechanized (Stranger, 1985). This resulted in water extraction more than tri-pling. As a result of this expansion it was notice-able that the adverse eects of increasing the number of wells was re¯ected in increased salinity levels in the groundwater reservoir as well as in the soils in the Batinah region (Northern coastal area).
Ad hoc expansion of this type ought to be con-trolled and not tolerated. In 1989, the government started a national campaign for water conserva-tion (Royal decree 72/89). Various water-use reg-ulations have been put into force and all wells have had to be registered. Since then, the objec-tive of the government has been to achieve sustainable agricultural growth that precludes horizontal expansion and additional water extrac-tion. Eorts, therefore, should be directed to vertical expansions, i.e. increased agricultural productivity and farmers' eciency.
New programs to introduce modern irrigation techniques were promoted to improve irriga-tion eciency. As of 1995, 1800 farms were equip-ped with new irrigation systems and more than 2500 farms are currently at the design stage. This program was supported with a subsidy scheme ranging from 30 to 75% of the installation cost depending on the farm size (Zaibet and Omezzine, 1997). Currently 5±6% of the agricultural area is equipped with modern irrigation systems (Zaibet and Omezzine, 1997). But even where advanced techniques have been adopted, irrigation eciency at the farm level remains low (MAF, 1993).
The problem of agricultural development in the Arab Gulf countries is primarily a problem of management of irrigation water and production eciency. The above-described set of support policies (direct payments and protection) would not be eective without programs to increase managerial skills and production eciency at the farm level. Despite all the support policies it is recognized that agricultural productivity has remained relatively low (Zaibet and Omezzine, 1997; Omezzine et al., 1999).
This paper analyzes horticultural growers' tech-nical eciency in Oman, a country where agri-culture is substantially subsidized. The paper focuses on the Batinah region, which represents about 50% of the total cultivated area in Oman and where the problems of water scarcity and soil salinity as a result of excessive pumping of groundwater are acute. We use dierent methods to estimate eciency indexes: (1) the stochastic production frontier (SPF); (2) the data envelop-ment analysis±Charnes, Cooper, Rhodes (DEA± CCR) model; and (3) the DEA±Banker, Charnes, Cooper (BCC) model. The DEA models (CCR, BCC) allow the investigation of returns to scale for individual farms. We are interested, in par-ticular, in the eects of structural variables such as farm size, o-farm income and soil type on pro-ductivity and on the level of eciency. If sig-ni®cant ineciencies exist the identi®cation of factors contributing to such ineciencies is very important for policy decisions.
1.1. Data description
The study was conducted in the Batinah region as a major agricultural area of Oman. This reg-ion has bene®ted the most from government pro-grams to install new irrigation systems (drips and sprinklers). Farm data were collected through a questionnaire. A total of 50 farmers selected ran-domly were interviewed, but only 35 observations were used because of missing information. Farm-ers were growing a variety of horticultural crops throughout the year: tomato, water melon, sweet-melon, cucumber and potato. Output prices were collected for individual farms; thus, we aggregated all outputs into one output value (the dependent variable). In this study we used three endogenous variables: labor, capital and water.
Water quantity (in m3) was estimated based on
irrigation application design and farmer's irriga-tion schedule. Irrigairriga-tion schedule (interval and period of irrigation) for each crop was collected by interviews. Drip emitters on each farm are designed to deliver a ¯ow rate of 4 l hÿ1. Capital
of occasional and permanent labor. Finally, we included land (total plot size) as an exogenous input in the production function.
Other explanatory variables were measured as dummies to explain agricultural productivity as measured by (log) total output. Farmer's age is included as a dummy variable equal to 1 if age is less than 30 years and 0 otherwise. The age vari-able serves to test the hypothesis that younger people are more receptive to innovations and therefore a positive impact on productivity results. Farm size is included as a dummy, which is equal to 1 if the size is less than 10 feddan (1 fed-dan=0.42 ha) and 0 otherwise. The relation between farm size and eciency has received a great deal of attention among economists but the empirical evidence is not conclusive (Bagi, 1982; Ellis, 1992; Kalaitzandonakes et al., 1992).
We also included o-farm income in the analysis of farmers' eciency. It is hypothesized that farmers having extra-farm income would spend less time on the farm (Kumbhakar et al., 1989). On the other hand, we can posit that the existence of o-farm income may increase farmers' e-ciency as o-farm income may provide farmers with cash which is necessary to buy inputs and hire adequate labor. The nature of the soil may also aect eciency. Adequate soils are more likely to yield a higher eciency than soils which are not adequate (salinity or other problems). We also included the farmer's experience as a dummy variable that equals 1 for more than 5 years and is 0 otherwise. In fact, many farmers in Oman, mainly the youngest, have not grown up on farms. Lack of experience may be a source of ineciency.
2. The stochastic frontier and eciency measures
The SPF was ®rst introduced by Aigner et al. (1977), and Meeusen and van de Broeck (1977). Jondrow et al. (1982), who extended the SPF to allow for the estimation of individual ®rm-eciency levels with cross-sectional data, intro-duced a major development in the SPF. Since then, the SPF has been widely used in empirical work. Recent applications include the estimation of farm eciency in US dairy farms (Kumbhaker
et al., 1989), and technical eciency in commercial ®sheries (Kirkley et al., 1995), and technical e-ciency in banking (Caudill et al., 1995).
The SPF approach assumes that ®rms deviate from the production frontier due to ineciencies. The starting point to formulate our model is a traditional Cobb±Douglas production function, which is an excellent candidate for SPF (Kirkley et al., 1995; Kumbhaker et al., 1989):
YAX exp 1
In this equation,Yis output,Xdenotes a vector of inputs (endogenous and exogenous), andis a vector of parameters.Ais the eciency parameter and is the error term. But farms may deviate from the production frontier not only because of the usual random noise but also because of tech-nical ineciency. To accomplish the link between the eciency parameter and the SPF, A is speci-®ed as (Aigner et al., 1977):
A exp
So, Eq. (1) becomes:
YX exp 2
In Eq. (2)is a parameter common to all farms and is the technical ineciency measure that varies across farms. The ®rst error term is assumed to be a two-sided error that accounts for factors outside the farm control, whereas the sec-ond, , is assumed to be a one-sided error asso-ciated with factors under the control of the farm.
Estimation of Eq. (2) is based on a speci®c assumption about the distributions of the error terms and . The most common distributional assumptions made are the normal and half-normal distributions forand, respectively.
Let; the density function forcould be written as (Weinstein, 1964):
f 2
l
; 3
where 2212;l= and, and are
Following Jondrow et al. (1982) the technical ineciency is estimated as the conditional expec-ted value ofgiven:
E =
l= l== l= 4
The log likelihood function of Eq. (3) for a sample ofNfarms is:
ln LX We can also transform Eq. (4) to be a function of
and l instead of and . After algebraic
transformation, Eq. (4) could be written as:
l
1l2 l= l== l=: 6
Eq. (5) is estimated using TSP 4.3 (Time Series Processor). Then, the parameter estimates, l, , and, are used to estimate, as in Eq. (6). Tech-nical eciency measures are then derived as exp(ÿ).
The stochastic production frontier method allows for technical eciency to be measured for individual farmers from the statistical noise as shown in Eq. (6). Such measures will provide important information on the level of eciency. Moreover, estimation of Eq. (5) would show the sources of ineciency, namely the eect of the dierent exogenous variables, such as farm size, farmer's age, o-farm income, quality of soil, on productivity and eciency.
2.1. Estimation and results
Maximum likelihood estimates of Eq. (5) are presented in Table 1. There are three endogenous variables, i.e. capital, labor, and water, and one exogenous variable, i.e. land, all in natural
logarithms. Exogenous variables which aect productivity are included as dummies, so special attention should be given to the interpretation of these variables given the semi-logarithmic nature of the overall equation (Kennedy, 1982).
All parameter estimates were statistically sig-ni®cant at the 5% signi®cance level except for land. The coecient of labor has the highest value (elasticity) followed by irrigation water and capi-tal. This suggests that productivity would be higher when more hired labor exists. All four exo-genous variables aecting productivity have sig-ni®cant coecients. The coecient of variable age suggests that productivity for young farmers is 34% [1ÿexp(ÿ0.42)] lower than that for older ones. So, contrary to what could be expected, young farmers are not more productive.
In the context of Oman, farming does not depend on the expertise of the owner, but rather on hired labor. Most young farmers have extra-farming occupations, which may explain the negative rela-tionship between age and productivity. The same could be said about the variable experience (EXP)
Table 1
Maximum likelihood parameter estimates of SPF
Variablea Parameter
estimate
Standard error
T-statistic
Intercept ÿ2.86 0.63* ÿ4.50
Capital 0.30 0.02* 11.07
Water 0.66 0.02* 22.41
Labor 0.85 0.08* 9.72
Land 0.03 0.07 0.52
Age ÿ0.42 0.15* ÿ2.76
Exp. ÿ1.07 0.07* ÿ13.39
Income 0.75 0.14* 5.28
Size ÿ0.49 0.12* ÿ4.06
Soil 0.40 0.11* 3.5
0.18 0.001* 116.7
a Endogenous variables are in natural logarithms and
which shows a negative sign as well. The number of years in farming does not seem to lead to bet-ter managerial skills as acquired over the years. O-farm income, however, indicates a gain in productivity. Previous studies focused on the negative eects of o-farm activities as an indica-tion of eort spent on non-farm activity (Kumb-haker et al., 1989). In our case, farmers with o-farm activities tended to hire more labor, available at low wages, to compensate for the time they spent outside the farm, which tends to increase eciency. Another outcome of the model is the negative sign associated with the variable size. Smaller farms (less than 10 feddans) are 38% [1ÿexp(ÿ0.49] less ecient than larger farmers. Finally, soil type indicated a positive eect on productivity. Loss in productivity due to inade-quate soils is evaluated at 49%.
The other parameters of the model,land, are also signi®cantly dierent from zero at the 5% level. The ratio of standard errorslis found to be greater than one (1.39) and signi®cantly dierent from zero, indicating that the residual of the production frontier is dominated by technical ineciency. Technical eciency measures are derived using Eq. (6) and are reported in Table 2. Frequency analysis shows that there is a group of farmers (about 50%) where technical eciency falls below 40%, whereas only 20% of farmers have a technical eciency level of more than 70%. Parameter estimates (Table 1) show that factors such as o-farm income and soil quality positively in¯uence the level of productivity, whereas small farm size and age showed negative relationships with productivity. Further insights into the eects of these factors are explored using the DEA method. In particular, the eect of farm size could be in¯uenced by the presence of returns to scale.
2.2. DEA approach
DEA is a nonparametric data-based methodol-ogy that provides measures of optimal pro®t ratios and practice eciency. It identi®es the best-practice ®rms on the ecient productivity frontier (ecient ®rms) and ®rms that are interior to that frontier (inecient ®rms). Many outputs and
inputs can be analyzed simultaneously for an arbitrary number of observations, also called decision-making units (DMUs). Relative eciency characterizations can be made DMU-by-DMU across all of the DMUs under consideration, for the same inputs and outputs of data. The selected DMU for comparison is denoted by DMUc. The
input/output data entries must be non-negative, with zero entries allowed.
Two DEA models (input-oriented) of relevance for a wide range of applications are: (1) the ratio model, which assumes constant returns to scale; and (2) the convex model, which allows increas-ing and decreasincreas-ing returns to scale. In DEA litera-ture, the ®rst is CCR model (Charnes et al., 1978), and the second is the BCC model (Banker et al., 1984). Both models are linear programming (LP) formulations. In this part of the paper we com-pare and contrast technical eciency measures in CCR and BCC models and investigate returns to scale associated with the sample of farms. As dis-cussed earlier, here we also show the eects of o-farm income, o-farm size, and soil type on the e-ciency measures, using experimental designs.
2.3. Fundamentals and concepts
A DEA data domain consists ofn DMUs. The selected DMUc (c=1,2,. . .,n) is characterized by
an input vector Xc and an output vector Yc. (All
vectors are column vectors, and [. . .]T stands for
transpose.) For the rest of the paper we use the following de®nitions and notations:
Yc=[y1c,y2c,. . .,yrc]T,r-dimensional non-negative
output vector, wherec=1,2,. . .,n.
Xc=[x1c,x2c,. . .,xmc]T,m-dimensional
non-negative input vector, wherec=1,2,. . .,n.
U=[u1,u2,. . .,ur]T,r-dimensional non-negative
output multiplier.
V=[v1,v2,. . .,vm]T,m-dimensional non-negative
input multiplier.
l=[l1,l2,. . .,ln]T,n-dimensional vector of real
scalars.
I=[1,1,. . .,1]T,n-dimensional vector of 1s.
X=[X1,X2,. . .,Xn], input data matrix.
Y=[Y1,Y2,. . .,Yn], output data matrix.
w= U
V
fc(w)=UTYc, virtual bene®t to DMUc,
c=1,2,. . .,n.
gc(w)=VTXc, virtual cost to DMUc,
c=1,2,. . .,n.
fc(w)/gc(w), DEA bene®t-to-cost ratio for DMUc
with respect to multiplierw, wheregc(w)>0;
Wcis the set of all such multipliers, and
dim(Wc)4r+m.
Thompson et al. (1993) introduced the following eciency measure for DMUcunder the CCR ratio
(multiplier) model:
Maxfc w=gc w
s:t: fj w4gj w;w50;
7
wherej=1,2,. . .,c,. . .,n.
Table 2
Eciency measures from SPF, CCR and BCC modelsa
Farmer (SPF) (CCR) Class (BCC) Class LB±RTS UB±RTS Returns
1 0.2523 0.364135 N 0.877842 N
2 0.0342 0.238095 N 1 E 0.927536 1 Incr.
3 0.2036 0.132683 N 0.840312 N
4 0.2797 0.713443 N 1 E 0.442355 0.483790 Incr.
5 0.3572 0.344912 N 0.558796 N
6 0.6336 0.466889 N 1 E 0.624223 0.770309 Incr.
7 0.3676 0.477821 N 0.735618 N
8 0.0361 0.021719 N 0.832573 N
9 0.0351 0.082039 N 0.737607 N
10 0.3427 0.184336 N 1 E 0.883091 1 Incr.
11 0.8902 0.443661 N 0.520437 N
12 0.5307 0.154745 N 0.244985 N
13 0.1718 0.210767 N 1 E 0.960638 1 Incr.
14 0.1141 0.536298 N 1 E 0.485160 1 Incr.
15 0.3057 0.235349 N 0.519075 N
16 0.9744 0.915631 N 0.978353 N
17 0.4138 0.744705 N 0.759211 N
18 0.8209 0.252826 N 1 E 0.760520 1 Incr.
19 0.0460 0.098799 N 1 E 0.911489 1 Incr.
20 0.2568 1 E 1 E ÿ0.481842 0.225143 Const.
21 0.4126 0.599027 N 0.879788 N
22 0.4029 0.489796 N 0.782011 N
23 0.9493 0.214119 N 0.259071 N
24 0.9583 1 E 1 E ÿ1.483871 0.171692 Const.
25 0.9804 1 E 1 E ÿ7.07E+16 1 Const.
26 0.6676 0.776303 N 0.823893 N
27 0.4288 1 E 1 E ÿ0.964678 0.204244 Const.
28 0.4107 1 E 1 E ÿ0.778442 0.900360 Const.
29 0.4795 0.972378 N 1 E 0.041950 0.896381 Incr.
30 0.4400 0.35458 N 1 E 0.708078 1 Incr.
31 0.2112 0.399863 N 0.721441 N
32 0.0815 0.21335 N 0.605688 N
33 0.9532 1 E 1 E ÿ7.07E+16 0.203094 Const.
34 0.1557 0.556212 N 0.727466 N
35 0.1787 0.214664 N 0.62489 N
a SPF, stochastic parametric frontier; CCR, Charnes, Cooper, Rhodes; BCC, Banker, Charnes, Cooper; LB, lower bound; UB,
upper bound; RTS, returns to scale; Incr., increasing returns; const., constant returns;is the optimal value ofin CCRE and BCCE
Using the Charnes±Cooper transformation (Charnes and Cooper, 1985), one can obtain the following LP formulations:
1. Input-oriented CCR ratio model for DMUc
CCRE: envelopment
2. Input-oriented BCC convex model for DMUc
BCCE: envelopment
Now, we state the CCRE and CCRM models with slack variables added to the constraints. Similar formulations can be done for BCCE and BCCM models. unrestricted m
i1vixic1 10
Here, sÿ
i and s+k are input and output primal
slacks, and tj are dual slacks. , and u* are real
scalars.
The eciency rating of DMUc in the CCR or
BCC model is denoted by the optimal valuec*. A
DMUc with c*=1 is said to be `scale ecient'
and in class RE. This class can be partitioned into three sub-classes: (1) DEA-extreme-ecient DMUs in class E, which are at the vertices on the frontier; (2) DEA-non-extreme-ecient DMUs in class E0, which are on the frontier between ver-tices; and (3) DEA-inecient DMUs in class F which are on the extended frontier. Firms with 0<c*<1 are said to be scale inecient and in class
N (Charnes et al., 1991).
Furthermore, DMUcis in E iflc*=1, lj*=0, for
Scale-ecient classes E and E0 are also called
`technically ecient'. Class F is scale ecient but not technically ecient because the optimal slacks are present, and class N is both scale and tech-nically inecient. In this paper, E[E0 forms the `technically ecient' class, and F[N forms the `technically inecient' class. ([stands for the union of two disjoint classes.)
3. Returns to scale (RTS) for DMUocan be
found by solving the following LP models (Banker and Thrall, 1992):
u
u unrestricted;
U50;V50
uunrestricted;
U50;V50 11
It follows that,uÿ*4u*4u+*, anduÿ*41. DMUc
displays (1) increasing RTS, ifuÿ*>0; (2)
decreas-ing RTS, if u+*<0; and (3) constant RTS, ifuÿ*<0
andu+*>0.
2.4. Empirical results
We used matlab to formulate the LP models
the BCC model were computed and presented in Table 2. Lower-bound (LB) and upper-bound (UB) form the RTS interval, with increasing returns and constant returns for the respective extreme-ecient farmers in the BCC model.
The classes E0 and F are empty in both CCR and BCC models. Hence, class E contains the technically ecient farmers and class N includes the technically inecient farmers. Note also that farmers 20, 24, 25, 27, 28, and 33 are technically ecient in both models, and they display constant returns, while 10 other technically inecient farmers in the CCR model display increasing returns in the BCC model. They are farmers 2, 4, 6, 10, 13, 14, 18, 19, 29, and 30.
In the next stage of the analysis we consider an unbalanced general linear model (GLM) with the following ®xed factors with no interactions:
Factor-1: farm size coded as 1 (small), 2 (medium), and 3 (large); 1 if size is less than 10 feddans, 2 if size is between 10.01 and 20.00 feddans, and 3 if size is greater than
20.01 feddans;
Factor-2: soil type coded as 1 (adequate for crop), and 2 (not adequate for crop);
Factor-3: o-farm income coded as 1 and 0; 1 if farmer has income outside farm, and 0 if he has no such income; and
Response variable: eciency rating in CCR model, as a percentage.
The purpose of this analysis is to determine whether there are any dierences in the mean e-ciency ratings among the categories of each of the factors. For example, any dierences in mean e-ciency ratings among the levels of factor-1 will reveal the signi®cance of government subsidies (given to farmers based on farm size) on farmers' eciency.
We consider the following model:
Yijklaibjckeijkl; i1;2;3;
j1;2; k1;2; l1;2;. . .;nijk
GLM1
Here, a,b, andcrepresent the eects due to fac-tors 1, 2, and 3, respectively, Y is an eciency
rating,is an unknown constant,nijkis the
num-ber of eciency ratings in cell (i, j, k), and e
represents the random error term. There are 12 treatments in the model with farmers being the experimental units and their eciency ratings forming the samples drawn at random. Some samples are unequal in size, and hence the model is unbalanced.
We used minitab to analyze this model and
the following results were obtained at 0.05 level of signi®cance.
1. There were signi®cant dierences in mean eciency ratings among farm size levels (H0:
a1=a2=a3=0;p-value=0.008)
2. There were signi®cant dierences in mean eciency ratings among soil type levels (H0:
b1=b2=0;p-value=0.028)
3. There were no signi®cant dierences in mean eciency ratings among o-farm income levels (H0:c1=c2=0;p-value=0.137)
We analyzed the model again, having drop-ped the insigni®cant factor o-farm income, and obtained the following results:
The model:Yijkaibjeijk;
i1;2;3; j1;2; k1;2;. . .;nij
GLM2
1. There were signi®cant dierences in mean eciency ratings among farm size levels (H0:
a1=a2=a3=0;p-value=0.014)
2. There were signi®cant dierences in mean eciency ratings among soil type levels (H0:
b1=b2=0;p-value=0.04)
In the next stage we analyze the unbalanced GLM2 with the above factors and eciency ratings in the BCC model as the response variable. The following results were obtained:
1. There were no signi®cant dierences in mean eciency ratings among farm size levels (H0:
a1=a2=a3=0;p-value=0.316)
2. There were no signi®cant dierences in mean eciency ratings among soil type levels (H0:
The model assumptions in the above GLMs, namely, independence of samples, homogeneity of population variances, and normality of random error terms, were checked and veri®ed. As can be seen from the above, both farm size and soil type in¯uences signi®cant dierences in CCR eciency ratings but not in BCC eciency ratings. The major dierence between the two models is that there are 10 technically ecient farmers in the BCC model who display increasing returns. These farmers were technically inecient in the CCR model with constant returns. This fact may have been captured in the change in results of GLM2 with respect to farm size and soil type. In other words, size of the farm and adequacy of soil became insigni®cant.
3. Summary and conclusions
The study of eciency in resource allocation has been the focus of the neoclassical theory of pro-duction. When ineciency arises this does not only imply sub-optimal allocation of resources, as valuable as water in Oman, but also may lead to rural income deterioration and farmers' bank-ruptcy. In an oil-exporting country such as Oman, when farm income is low, even with the con-tinuous support of the government, rural farmers may ®nd better opportunities in non-farm activ-ities where better and more stable income is avail-able. This scenario calls for the need to investigate ways to improve agricultural returns by improving farmers' eciency.
This paper derived technical eciency indexes for a sample of farmers in the Batinah region of Oman using the stochastic production frontier and the DEA methods. Dierent methods are used because the determinants of technical eciency may be in¯uenced by the method used and also by the assumptions (such as returns to scale) main-tained. Results from the SPF and DEA±CCR models show that the percentage of farmers that could qualify as technically ecient is as low as 17%. When the DEA±BCC method was used this percentage increased to about 46%.
Furthermore, we investigated the factors that determine the level of productivity and eciency.
The main ®nding from the study is that farmer's age, number of years in farming, farm size and soil type, have signi®cant eects on productiv-ity. While farm size and soil type signi®cantly in¯uence farmer's eciency as found in the DEA± CCR model under constant returns to scale, this eect seems to disappear when using the DEA± BCC approach, i.e. when increasing returns to scale exist. This result seems particularly interest-ing. As an additional 10 farmers reached the e-cient frontier with increasing returns to scale, the mean eciency ratings for all farmers across the levels of farm size and soil type became equal. This methodological aspect re¯ects that varia-tions in the level of eciency for a given sample of farms as well as the impact of exogenous variables depend largely on the choice of the method used.
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