1
RELATIVE EFFICIENCY OF FOOD FARMERS IN THE NIGER DELTA AREA:
APPLICATION OF PROFIT FUNCTION ANALYSIS
By
F.O.IDUMAH1 and F.Y. OKUNMADEWA2
1Forestry Research Institute of Nigeria, Ibadan
2Department of Agricultural Economics
University of Ibadan, Ibadan
Abstract
The study examines the efficiency of food crop farmers in Niger Delta area where there have
been cases of land degradation resulting from oil pollution. Data used for the analysis were
obtained from 270 farmers through multistage sampling procedures. Results show that 53% of
the farmers have formal education, 92% have more than five years of farming experience, 23%
purchased land for farming while average farm size is 1.53hectares. The normalized profit
function shows that the coefficients for fixed capital; farm size, labour and planting stocks were
significant (P 0.001). The study establishes that farmers in non-polluted area are more
economically efficient than those in polluted areas. However, the study concludes that farmers in
both polluted and non-polluted areas utilize their resources below the optimal level.
Key words: Oil pollution, relative efficiency, Niger Delta, Food crop farmers.
1.0 Introduction
There is a growing realization that the attainment of optimum food production goes
beyond the question of availability of improved production technologies (Oyekale2001). The
natural resources base upon which crop production ultimately depends must be properly
managed and conserved. Sustainability, which is the combination of production with
conservation of resources to meet the needs of future generations, is therefore the key to land
management (Young 1998).
The land resources as reflected in the extent and character of soil and prevailing
economic condition form the basis for agriculture. It imposes constraints on the amount of food
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that human societies can produce and the way in which they produce it. Unfortunately, land is no
longer abundant and its productive potential in being reduced by degradation. Land degradation
has been defined in different ways by different authors based on their perspectives. African
Development Bank (ADB) defined land degradation as a reduction in the productivity of land
resulting form soil loss, breakdown in soil structure salinization, water logging, nutrient loss, and
pollution from toxic substance ADB (1995). Bojo (1996) distinguished between land and soil
degradation. Soil degradation means the total or partial loss of soil productivity quantitatively,
qualitatively or both as a result of such processes as soil erosion, salinization, water logging,
depletion of plant nutrients, deterioration of soil structure, desertification and pollution. On the
other hand, land degradation embraces a wider concept and means the deterioration of soil,
vegetation and water of resources both in quantity and quality.
Land degradation is caused by a number of factors. Scherr and Yadav (1996) highlighted
some possible causes of land degradation, which include deforestation, water and soil erosion,
water logging, salinization, and removal of organic materials, bush burning and misuse of agrochemical. Lal (1995) listed indiscriminate and intensive land use for seasonal crop production,
resources based production system with little or no purchased inputs to replenish nutrients
harvested in crops and animals and inherently low soil nutrient and harsh environment as other
causes of land degradation. One major cause of land degradation in Nigeria is oil spillage
causing land pollution, which is a common feature in oil producing states in the Niger Delta
region of the country. NEST (1991) identified mining and quarrying which result in oil spillages
and gas flaring as major causes of land and environmental degradation causing destruction and
reduction of agricultural and related activities in Nigeria.
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Land pollution by crude oil as a result of spillages from on-shore and off shore operations
of the petroleum industries affects the flora, faunas that normally enhance soil fertility and
aquatic animals, which constitute major protein reserves for man (Fagade 1981). Don Pedro et al
(1995) however observed that the biological effects of crude oil on the environment vary with
the chemical constituents of the spilled oil, the flora and fauna involved atmospheric condition
topography and mode of oil spills.
There had been many cases of oil spillages and gas flaring in the oil producing state that
give room for a lot of concern. Many studies had been conducted to determine the effects of oil
spill on flora Amadi 1990 and, Don Pedro et al 1994, 1995). Osuji (1998) also reported that oil
spillage resulted in fewer plant covers when compared with area will no oil spillage. Fubara
(1987), Maduka, (1998) and Eromosele (1998) reported on the extent of environmental damage
resulting from oil pollution in the Niger Delta region. Adeniyi et. al (1983) in a study on the
socio-economic impacts of oil spillage in the petroleum producing riverine area of Nigeria
reported the negative impact of this problem on the farming and fishing activities in the area.
Okezie and Okeke (1987) in a study of the environmental impact of gas flaring on Izombe flow
station in Imo State revealed staggering evidence of environmental damage resulting in low
agricultural productivity of the affected area. The major objective of this paper is to examine the
efficiency of farmers in polluted and non-polluted areas of the Niger Delta.
2.0 RESEARCH METHODOLOGY
2.1
Data
Data used for this study were collected from 270 food crops farmers (140 from oil polluted area
and 130 farmers from non- oil polluted area) in 31 villages in Rivers and Delta States of the
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Niger Delta Region of Nigeria through multi -stage sampling procedures. The data covered
socio-demographic characteristics of the farmers, types of crop grown labour used, membership
of association, sources of fund for farming, land ownership status, incidence of oil pollution,
prices of output and wages.
2.3 Empirical Model
Input demand in polluted and non-polluted areas was studied using the profit function
approach suggested by Lau and Yotopoulos (1979).
For the purpose of this study, farm
households were divided into two groups, according to whether they are farming in oil-polluted
or non-polluted areas. Farms are assumed to have fixed endowments of Land (ZL) and Capital
(ZK), which cannot be varied in the short run. However, they can choose variable amount of
Labour (L), Planting Materials (PM) and Fertilizer (F) the prices of which are CW, CM, and CF
respectively. The profit and the cost of the variable inputs are normalized by the prices of the
output.
The normalized profit function using the output price can be written in a generalized form
as:
m
π*(q, Z) = F{Xi* (q,Z)….Xn*(q,Z) –ΣqjX*j(q,Z)
j=1
Where:
qj = Normalised factor prices.
F = Well behaved production function.
X = Vector of variable inputs.
Z = Vector of fixed inputs.
(1)
5
Starting with any well specified normalized restricted profit function; direct application
of Hotellings-Shephard’s Lemmas to the function yields the corresponding demand
equation:
π*(q, Z)/qj = -X*j
j=1….m
(2)
Multiplying both sides by qj/ π* gives a series of m factor share equations:
π*(q,Z)/qj=-X*j/π*=ψj*
j=1……m
(3)
Equations 2 and 3 form the theoretical basis for the specifications of the empirical
models. Following previous studies (Yotopoulos, and Lau 1979; Khan and Maki, 1979;
Saleem, 1988; Duraisamy, 1990; Ajani and Olayemi,2000; Okoruwa et al, 2001), the
specification of the systems of equations of the normalized profit function and the factor
share equations is given as:
m
Ln πi*=LnA* + ψi*PPP + Σψi*LnCi + ΣβiLnZi +ei
J=i
(4)
-CiXi = ψi* PPP + ψi*NPPNP +ξi
πi*
(5)
Using the Cobb-Douglas assumption for technology, equations 4 and 5 can be re-written
in a Unit Output Price (UOP) profit function:
Ln πi*= LnA* +ψw*LnCw +ψF*LnCF +ψm*LnCM
+βl*LnZL+βk*LnZk +δ*D
(6)
The Factor Share equations (Input demand function) for the variable inputs is given by:
-CwXw/π
=
ψwNP
+ ψwP
(7)
- CFXF/π
=
ΨFNP
+ΨFP
(8)
6
- CMXM/ π
=
ΨMNP
+ ΨMP
(
9)
Where:
π * = Normalised average profit.
CW = Normalised average Wage Rate in each village (N/Manday).
CF = Normalised average price of fertilizer input in each village (N/Kg).
CM = Normalised average price of Planting Materials in each village (N/Kg).
ZL = Land used in crop production in hectares (ha).
ZK = Capital used to purchase fixed inputs like hoes, cutlass and other implements (N).
Di
= Dummy for soil condition 1 =non-polluted areas; 0 =polluted area.
ΨiP = parameters to be estimated for polluted areas.
ΨiNP = parameters to be estimated for non-polluted areas.
i , βi , δ
A are other parameters to be estimated.
3.0 Result s and Discussion
3.1Socio-economic characteristics of farmers
The socio-economic characteristics of the respondents are presented in Tables 1 and 2.
The average age in years for the farmers is 43.26. The highest percentage of farmers (71.9%) is
within the age bracket of 31 and 50 years. This shows that most farmers are still young. On the
gender aspect, male farmers are more than female farmers. The percentage of female farmers is
30.7per cent. This indicates that women involvement in farming in the study area is low. The
average family size is 5.18. This large family size implies availability of family labour to the
farmers. The literacy level of most farmers is relatively moderate with about 23% having no
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formal education while 18.1% have primary education. Over 53% of the farmers have postprimary education. The marital status of farmers shows that 13.7% of the farmers are singles
while over 80% are married.
Membership of co-operative societies is not very common among the farmers. Among
the respondents only 22.6% belong to co-operative societies. This shows that majority of the
farmers are not exploring the benefits accruable from co-operatives societies. The farming
experience of farmers shows that most of the farmers have been in the farming business for an
average of 16years. Resulting from the vagaries of farming operation due to unfavourable
environmental condition in the study area, 57% of the farmers engage in other jobs like fishing,
trading etc, to supplement income from farming activities. The farmland ownership structure
shows that most respondents (64.1%) farm on communal and leased lands.
The average farm size cultivated by the farmers in the study area is 1.56 ha, the highest
percentage of farmers are within the range of 1-1.5 hectares. Average family labour used amount
to about 83.32 man-days per hectare. More family labour is used than hired labour. The average
wage paid hired labour wage is N400 per day. About 26.5 % of farmers used fertilizer on their
farms in the non-oil polluted area while 47.2% of farmers in the oil polluted area used fertilizer.
All the farmers in the area practice mixed cropping with over 50% planting between 4 to
7 different crops on the same plot. About 51.8% of the farmers attest to the pollution of their
farm with petrol- chemical products while 48.2% report that there was presence of oil pollution
in their farmlands
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Table1. Socio-economic characteristics of Farmers
Characteristics
Gender
Age (years)
Education
Religion
Farming Experience(years)
Marital Status
Family size
Access to farmland
Farming System
Incidence of oil pollution
Operationalization
Male
Female
<20
21-30
31-40
41-50
51-60
61&above
No Education
Primary Education
Secondary Education
Tertiary Education
Vocational Education
Traditional Religion
Christianity
Islam
0-5
6-10
11-15
16-20
21-25
26 & above
Single
Married
Divorced
Widow/Widower
1-5
6-10
(i) Family Land
(ii) Communal Land
(iii) Rented Land
(iv) Purchased Land
(v Family and rented land
Mixed Cropping
Agro forestry
None
Presence of oil pollution
Frequency
187
83
0
37
72
85
56
20
62
49
78
65
16
Percentage
69.3
30.7
0
13.7
26.7
31.5
20.7
7.4
23.0
18.1
28.9
24.1
5.9
10
260
0
34
55
57
33
30
61
37
195
5
33
156
114
97
31
77
35
30
258
12
130
140
3.7
96.3
12.6
20.4
21.1
12.2
11.1
22.6
13.7
72.2
1.9
12.2
57.8
42.2
35.9
11.5
28.5
13.0
11.1
95.5
4.5
48.2
51.8
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Table 2. Summary Statistics of Socio- economic Characteristics of Respondents
Variables
Age
Family Size
Years in
Schooling
Years in
Farming
Farm size
Family labour
(man days)
Hired labour
(man days)
Quantity of
Fertiliser
Used(kg)/ha
Total output
(kg)/farmer
AverageGross
Revenue(N)
Total Cost(N)
Oil Polluted Soil Environment
Sample
Min.
Max.
S.D
Mean
Value Value
42.43
20
59
10.723
5.056
1
9
2.1330
Un- Polluted Soil Environment
Sample Mean Min.
Max.
SD
Value
Value
42.95
20
45
11.334
5.34
1
9
1.989
9.93
0
19
7.072
11.66
0
19
6.601
16.56
2
32
8.53
15.99
5
27
8.461
1.53
82.78
0.2
10
6.07
215
0.8650
44.45
1.59
83.82
.2
11
5.89
200
1.0285
42.34
2.95
0
15
2.74
3.55
0
15
3.3709
23.23
16.68
836.46
1546.67
28833.73
5000
300000
33066.1 33361.51
5000
200000
30290.0
7516.46
1200
28200
5064.36 8022.38
1600
24600
6510.57
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3.2 Empirical results
The Zellner’s Seemingly Unrelated Regression Estimate (SURE) of the restricted normalized
profit function is presented below
LnY = -0.0552lnCF +0.04152**lnCL- 0.2409*lnCP + 0.03534lnZL + 0.6465**lnZK
(0.0539)
(0.0864)
(0.0761)
(0.0362) (0.0374)
-0.1985*D
(0.0221)
R2 = 0.69
* 1 percent level significant
Fixed inputs comprising farmland and capital carried a negative and positive sign as expected.
This implies that increase in farm size and other fixed inputs employed in production, would
increase profit. An increase in the use of farm size and fixed assets-(capital) by 10% will
increase profit by 3.5 and 6.4 percent respectively.
The value of the coefficient of pollution represented by the dummy was found to be
negative but significant at 1percent level. This suggests that farmers in polluted areas (PAs) are
less technically efficient than their counterparts in non- polluted areas (NPAs), that is, they
produce lesser quantity of output from the same amount of inputs. Given the same level of
inputs, farmers in PAs are 21 percent worse off in profit than their counterparts in NPAs.
Variables that were significant at 1 per cent level include fixed capital, farm size, labour
and planting stocks. The coefficients for fertilizer though complied with a priori expectation by
signing negative; it was not significant in increasing the profit level. Farm size is significant at 5
percent level but negative. Figures in parenthesis are the standard errors. The explanatory
power of the model shows the coefficient of multiple determination (R2) of 0.69. This implies
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that 69 percent of the variations in the dependent variables are explained by the explanatory
variables.
Input demand of the variable inputs
Equations 11-13 below show the result of the input demand function (factor share equation) for
the variable inputs. (See Tables 3 and 4)
CF/π =
- 0.8600P* (0.1157)
0.0241 (1-P)
(0.1205)
(11)
CL/π =
-0.9466P*
(0.0474)
0.7826*(1-P)
(0.0492)
(12)
CM/π =
-0..1859P* (0.0123)
0.2320*(1-P)
(0.0128)
(13)
-
All the coefficients of the input demand function were found to be significant at1percent except
that of fertilizer in polluted areas, which was not significant. The coefficients are – 0.02, - 0.78,
and – 0.23 respectively for fertilizer, labour, and planting stocks in NPAs and – 0.86, - 0.94, and
-0.18 in PAs. This shows that for a N1 increase in profit, fertilizer cost, labour cost and planting
stock would have to be increased by 2k, 78k and 23k in NPAs while the corresponding values
are 86k, 94k and 18k for PAs,. This shows that the cost of fertilizer is higher in PAs than in
NPAs. This is plausible since the demand for fertilizer will be more in PAs than in NPAs
probably to ameliorate the effects of soil degradation. The result also shows that labour cost is
higher in PAs than in NPAs. This could be as a result of displacement of hired labour from the
area because of the problem of oil pollution coupled with the competition for labour from the oil
industry, which may affect the number of labour available for farming. However, the cost of
planting materials is higher in NPAs than in PAs probably because farming is more profitable
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and feasible in NPAs than in PAs and this necessitated more demand for planting materials thus
prompting the higher cost.
TABLE 3: JOINT ESTIMATION OF THE RESTRICTED NORMALISED PROFIT FUNCTION
Variables
Parameters
Single
Equation
OLS
Unrestricted
(SURE)
Labour(LNCw
ψLCL
Planting Stock
ψPMCPM
0.6063**
(0.0781)
0.3917**
(0.1292)
0.5872**
(0.07849)
0.2578**
(0.1297)
Fertiliser
ΨFCF
Farm Size
β1
Capital
β2
Dummy
Dum 1
δ1
0.0348
(0.0478)
0.03475**
(0.0539)
0.3547**
(0.1309)
0.2154
(0.01625)
R2 =65.2
R2 =64.4
F=39.25*
-0.0300
(0.04809)
-0.3587**
(0.1314)
0.4147**
(0.0541)
-0.1843
(0.1629)
R2 =61.4
R2 =60.8
F=27.38*
Zeller’s Method with Restriction
1ST
2nd
3rd
Restriction Restriction Restriction
Ψi*NP =Ψi*PA Ψi*NP =Ψi* β1 + β 2 =1
Ψi*PA =Ψ*i
Ψi*NP =Ψ*i
ΨF*PA =Ψi*
0.5872**
0.5872**
0.4152**
(0.0958)
(0.0905)
(0.0864)
0.2578**
0.2578**
-0.2469**
(0.0680)
(0.0680)
(0.0761)
-0.0300
(0.0547)
0.3587**
(0.1399)
0.4147**
(0.0363)
0.0000
R2 =60.2
R2 =59.3
F=43.24*
-0.0300
(0.054)
-0.3587**
(0.1399)
0.4147**
(0.0363)
-0.1842
(0.2019)
R2 =59.2
R2 =58.13
F=38.21*
-0.0552
(0.0539)
0.3534**
(0.0374)
0.6465**
(0.0374)
-0.1985
(0.2217)
R2 =64.3
R2 =63.8
F=49.27*
Source: Computed from field survey 2002
TABLE 4: FACTOR SHARE EQUATION FOR THE FOOD CROPS
Variables
Parameters OLS
NO
Restriction 1
Restriction 2
Restriction
Ψi*NP =Ψi*
*NP
*PA
(SURE)
Ψi
=Ψi
Ψi*PA =Ψ*i
Labour
ψwP
9.4666
9.4666
(0.4765) (0.4748)
ψwUP
7.8769
7.8769
(0.4945) (0.4927)
Fertiliser
ΨFP
8.600
8.600
(1.1621) (1.1598)
ΨFUP
0.2412
0.2412
(1.2060) (0.2015)
Planting
ΨPMP
1.8591
1.8591
Stocks
(0.1240) (0.1235)
ψPMUP
2.3202
2.3201
(0.1287) (0.1282
Source: Computed from field survey 2002
9.938
(0.5201)
9.942
(0.5119)
8.635
(1.2411)
8.635
(1.2411
1.8624
(0.1236)
2.1432
(0.1236)
9.4666
(0.4766)
7.8769
(0.4945)
8.600
(1.1621)
0.2412
(1.2081)
1.8591
(0.1240)
2.3202
(0.1280)
Restriction 3
β1 + β 2 =1
Ψi*NP =Ψ*i
ΨF*PA =Ψi*
9.4666
(0.4748)
7.8769
(0.4927)
8.600
(1.1598)
0.2412
(1.2011)
1.8591
(0.1235)
2.3202
(0.1281)
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Hypotheses testing
Hypotheses 1 –3 were tested using the Zeller’s method with restriction. In this regard, the
Seemingly Unrelated Regression Estimation (SURE) procedure involving a joint estimation of
the Normalized Profit Function and the Factor Share equations for variable inputs was employed.
The method provides an asymptotically efficient method of estimation, the efficiency of which
was increased by imposing known constraints on the coefficients of the equations.
The first hypothesis was that of equal relative economic efficiency. This implies that δ
equal to zero. That is:
Ho:
-
δ*PA = 0
The hypothesis of equal relative price efficiency implies that:
Ho: ΨFNP = ΨFPA
ΨLNP = ΨLPA
ΨPMNP = ΨPMPA
ΨFPA
=
ΨF
ΨLPA
=
ΨL
ΨPMPA = ΨPM
-
While the third hypothesis was that of constant return to scale implies that;
Ho: β1
+ β2
=1
The results of the hypothesis testing are presented in Table 5. All the hypotheses were
rejected based on the comparison of the computed chi-square with the tabulated values. The first
14
hypothesis (H1) that the economic efficiency of farms in non-polluted and oil-polluted areas are
equal is rejected at the 1- percent level of significance. Hence, we conclude that farms in nonpolluted areas are relatively more economic efficient than those in the polluted areas. Hypothesis
two (H2), which states that there is equal relative technical and price efficiency between farmers
in non-polluted and oil-polluted areas, was rejected. This is expected given the rejection of the
first hypothesis. Hypothesis three (H3) relating to constant returns to scale is rejected. This
implies that farmers in both non-polluted and polluted areas utilise their resources below the
optimal level.
Table 5: Test of Statistical Hypotheses For Farmers In Polluted And NonPolluted Areas
TESTED
HYPOTHESIS
MAINTAINED
HYPOTHESIS
δ*PA = 0
ΨFNP =ΨFPA
ΨLNP =ΨLPA
ΨPMNP=ΨPMPA
VALUE OF CHI-SQUARE LEVEL OF
SIGNIFICANCE
COMPUTED CRITICAL
83.21
16.81
0.10
58.64
16.81
0.10
40.99
β1
+
β 2 =1
ΨFNP =ΨF
ΨFPA = ΨF
ΨLNP =ΨL and
ΨLPA = ΨL
ΨPMNP =ΨP
ΨPMPA = ΨPM
Source: Computed from field surveys 2002
16.81
0.10
15
Conclusion
This study examines the effects of oil pollution on food production in the Niger Delta,
Nigeria and presents results of an empirical application of the profit function method used in
testing the relative efficiency difference between farmers in polluted and non-polluted areas. The
test of relative economic efficiency reveals that farmers in non-polluted areas are more efficient
than their counterparts in the polluted areas. The result is not quite surprising, as oil pollution has
been found to affect the efficiency of farmers (Oyekale et al, 2004). This calls for a pragmatic
effort on the part of the government and companies prospecting for oil in the area to develop a
comprehensive programme to remediate the impact of oil exploration in the area by
rehabilitating areas already degraded. Efficiency of the farmers in the area can be enhanced if
there is improvement in the level of technology that is available to them in terms of seeds and
agrochemical usage.
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