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African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 8 (6), pp. 001-006, June, 2020. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
 

 

Full Length Research Paper 

 

Effects of market deregulation on cocoa (Theobroma 

cacao) production in Southwest Nigeria 

 
Idowu, E. O., Osuntogun, D. A. and Oluwasola, O. 

 
Department of Agricultural Economics, Obafemi Awolowo University, Ile-Ife, Nigeria. 

 
Accepted 13 August, 2019 

 
In the 1970s and 1980s there was a consistent economic down turn and decline in aggregate cocoa output in 
Nigeria. This culminated in the introduction of structural adjustment programme (SAP) in 1986 to stem this 
trend. The objective of the paper was to evaluate the effect(s) of the deregulated policy measures on the 
cocoa industry in southwest Nigeria. Data were collected from six important cocoa producing Local 
Government Areas (LGAs) within the region and these were analyzed using descriptive statistics and 
regression techniques. The study found that after two decades of operating SAP and economic liberalization 
policy in the country, cocoa production still remains in the hands of smallholder operators with little 
application of chemical inputs to enhance output. The paper concludes that significant increases in 
aggregate cocoa output can be achieved through a combination of sustained increase in real producer 
prices, local currency devaluation and increased supply of chemical fertilizers. 

 
Key words: market deregulation, cocoa, southwest Nigeria. 

 
INTRODUCTION 

 
Prior to the 1970’s, the policy of government towards 
agricultural development in general and to cocoa 
production in particular in Nigeria was one of minimum 
government intervention. Governments’ involvement was 
mainly supportive of the activities of farmers and focused 
mainly in the areas of research, extension, export crop 
marketing and pricing activities (Manyong et al., 2005). 
The attitude of government was borne largely out of the 
prevailing economic policy of laissez faire inherited from 
the colonial masters. This was soon to change.  
By the middle to late sixties, the Nigerian government like 
other developing countries, in realization of the relative 
importance of cocoa and other agricultural exports to the 
economy, brought the input supply and produce 
marketing systems under the state official monopoly. 
Marketing Boards were set up to intermediate between 
the farmers and the international market. The objectives 
then were to (i) stabilise prices paid to the producers (ii) 
ensure public access and control over foreign exchange  
 
 
 

 
*Corresponding author. E-mail: eidowu@oauife.edu.ng 

 
 
 
 
earnings (iii) strengthen the marketing mechanisms (iv) 
create an ideological antipathy to private traders and (v) 
impose constraints on multinational enterprises (Delloitte 
et al., 1990).  
In spite of these laudable objectives, the monopolistic 
marketing structure erected in the name of Commodity 
Boards served as a great disincentive to cocoa farmers 
both in production and replanting (Idowu, 1986). As found 
out by several studies, the Commodity Boards represent-
ted agencies for taxation as the producer prices paid to 
the farmers were well below world prices (Oni, 1971; 
Olayide et al., 1974; Idachaba, 1990; Akanji and Ukeje, 
1995). Other factors that negatively influenced cocoa pro-
duction and marketing as argued by Delloitte et al. (1990) 
were the oil boom syndrome and relative over-va-luation 
of the Nigerian currency (Naira) to other curren-cies. 
Consequently as observed by Idowu (1986), the 1970s 
and 1980s witnessed a consistent decline in aggregate 
cocoa output. Various research efforts were carried out to 
find the appropriate policy response to-wards restoring 
cocoa production to the prime position it used to enjoy 
before the advent of crude oil boom. The literature on the 
determinants of cocoa production and marketing 
including the analysis of Nigerian agricultural pricing 
policies can be generally classified under three 

file:///C:\Users\user\Documents\REPUBLICATION\AGRICULTURAL%20SCIENCES\AppData\Local\Temp\www.internationalscholarsjournals.org


 
 
 

 

groups: the pre-SAP, during SAP and post-SAP studies. 
The pre-SAP studies that included Olayide et al. (1974); 
Idowu (1986), and Adegeye (1986) among others 
established a strong relationship between the aggregate 
cocoa output and producer prices. Based on this, the 
policy recommendations tended to favour price incentive 
strategies in the form of administrative upward review of 
producer prices and input subsidization as panacea to 
sustaining increased aggregate output of cocoa. Other 
important factors identified as influencing cocoa pro-
duction and marketing include bureaucratic problems 
associated with Commodity Boards (Delloittee et al., 
1990); socio- economic and agronomic factors like age of 
the farmers, age and size of plantation, institutional 
inadequacies of Research Institutes and the Cocoa 
Development Units (Adegeye, 1986; Idowu, 1986).  

In spite of the price increases however, the aggregate 
output of cocoa in Nigeria showed a consistent decline 
(Adegeye, 1986). The inability of price increases to en-
hance cocoa production was then was linked to the struc-
tural weakness in the Nigerian economy. The global 
economic depression of the 1980s had an adverse effect 
on the Nigerian economy. There were both internal and 
external imbalances created as a result of price distor-
tions (CBN/NISER, 1992). Various austerity measures 
adopted at the beginning of the 1980s like stabilization 
measures of 1982 along with the restrictive monetary 
policy and stringent exchange control measures of 1984 
proved ineffective (Ojo, 1994). The situation then called 
for a complete economic re-design that would ensure 
economic stability, restructure the pattern of production 
and consumption and ensure reasonable growth.  

In 1986, the government of Nigeria announced the 
adoption and implementation of a Structural Adjustment 
Programme (SAP) with four cardinal objectives as fol-
lows:(i) Restructuring and diversifying the productive 
base of the economy in order to reduce dependence on 
oil exports; (ii) Reducing the dominance of unproductive 
investment in the public sector; (iii) Encouraging non-oil 
exports especially agricultural ones; and (iv) Improving 
the sectors' efficiency and intensify the growth potential of 
the private sector.  
The SAP embraced exchange rate deregulation, libera-
lization of export trade, reduction in extra budgetary 
expenditure, withdrawal of subsidies and the privatization 
of public enterprises. Thus, deregulation placed much 
emphasis on the market forces in determining the prices 
of goods and services and allocating the resources within 
the economy. Therefore, the policy measures as they af-
fect agriculture ensued as follows: (i) The abolition of 
commodity Boards and the privatization of many agricul-
tural enterprises previously controlled by the government  
(ii) Market liberalization of agricultural exports and; iii) 
Foreign exchange liberalization and currency devalue-
tion.  
The effects of Nigerian deregulation policy measures on 

cocoa production both at micro and macro level have 

 
 
 
 

 

also been investigated by many. Prominent among these 
are Adegeye and Dittoh (1988); Idowu (1988); Adegeye 
(1991); CBN/NISER (1992); Alimi and Awoyomi (1995) 
and Akanji and Ukeje (1995). The most 
prominentanalytical techniques had been descriptive 
statistics of "before, during and after" effect approach; 
budgetary analysis and production response function 
analysis. Some of the major findings included: first, the 
increased cost of maintaining cocoa farms by about 
300% while producer prices increased by about 800% 
(Adegeye, 1991)., and second, the adoption of SAP gave 
an estimated positive gross margin of N1,585.00 per 
hectare in 1989 compared to negative gross margin of 
N105.00 per hectare in 1985. Also, the production 
function estimated by CBN/NISER (1992) indicated that 
the aggre-gate output of cocoa is determined by real 
producer prices, exchange rates, interest rates, farm 
wage rates, world prices, and SAP dummy variable. 

Nevertheless, all these SAP period studies had a major 
shortcoming in that the SAP periods covered by the 
studies were too short for any meaningful analysis and 
evaluation for long run policy formulations. Incidentally 
even after canceling the SAP, the government has conti-
nued to intensify the privatization of public enterprises, 
increased wages, liberalized export trade and increased 
spending on infrastructural support to the agricultural 
sector putting in place stringent fiscal and monetary 
policies (UNS, 2001). The production and marketing of 
cocoa has also witnessed its ups and downs with the 
dynamics in national economic policy. The dynamics that 
has taken place in the production and marketing of cocoa 
has however not been evaluated. Hence, the main 
objective of this paper is to evaluate the effect(s) of the 
deregulated policy measures on the cocoa industry in 
southwest Nigeria. 

 
RESEARCH METHODOLOGY 
 
Two sets of data were utilized for this study. The first set, which 
were primary data, were obtained from the cocoa farmers. Three 
hundred questionnaires were analyzed for the cocoa farmers. Six 
important cocoa producing Local Government Areas (LGAs) of Oyo, 
Osun and Ondo states in southwest Nigeria were randomly select-
ed for the study. These were Egbeda (Oyo); Isokan (Osun); Irewole 
(Osun); Ayedaade (Osun); Owena (Ondo); and Ile-Oluji (Ondo). In 
each of the LGAs, 70 questionnaires were administered and those 
considered analyzable for the purpose of this study were Owena 
(40); Ile-Oluji (58); Ayedaade (48); Irewole (66); Isokan (40) and 
Egbeda (48). The data collected included socioeconomic variables 
like age, farm size, annual output per farmer, educational level, 
family size, volume of business and experience with the aim of 
identifying how they influence cocoa production and to study any 
significant shift from other previous studies.  

The second set of data were secondary in nature and included 
the producer prices, aggregate fertilizer supply, the world prices, the 
national output, the foreign exchange rates and the lending rates 
over a period of 35 years (from 1970 to 2004). These information 
were obtained from the official records of the Central Bank of 
Nigeria (CBN), Federal Office of Statistics (F. O. S), the 
International Cocoa Organisation (ICCO) and the Ministries of 
Agriculture and Natural Resources of respective states. 



 
 
 

 
The data collected were analysed using various analytical tech 

niques. Descriptive Statistics was employed to analyze the socio 
economic characteristics of the farmers while Correlation and 
Regression Analyses were used to obtain the structural equations 
for cocoa output at farm level for each local government area 
sampled as well as fitting a cocoa production response function 
using the ordinary least squares estimation technique. Two models 
were specified and estimated. These were:  
Cocoa Output Function at Farm Level which was specified as 
 

Qo = f(X1, X2,Ei) ------------------------------------------- (1) 
 
where: Qo = output per farmer; X1 = man days of labour employed 

X2 = Intensity of chemical used for fumigation (obtained as the 

amount of chemical use divided by farm size). 
 
Both the linear and power functions were fitted. A priori 
expectations were that the variables X1, and X2 would bear positive 
signs. The model was estimated separately for each Local Govern-
ment Area and for the whole region. 

Cocoa Production Response Function which was also specified 
as 
 

Qs = f(Xa, Xb, Xc, Xd, Xe, Ei)---------------------------- - (2) 
 
where: Qs = aggregate output of cocoa in year t; Xa = one year 
lagged real producer prices (1985=100); Xb = one year lagged 
exchange rate (dollar to Naira); Xc = one year lagged world price 
(N); X = aggregate fertilizer supplied in year t (metric tonnes); Xe = 
lending rates in year t (%); Ei = stochastic disturbance of zero mean 
and constant variance.  

Both the linear and double -log functions were tried. A. priori 
expectations of the model were that the coefficients Xa, Xb Xc, and 
Xd would bear positive signs while Xe would bear a negative sign.  

The two models specified were estimated by employing the least 
squares (OLS) estimation regression technique. In each case, the 
correlation matrixes of the variables were first obtained to observe 
the existence of multi collinearity problems. Also all the assump-
tions of the classical normal linear regression model were assumed 
to hold.  

The insight obtained from the correlation matrix helped in the 

formulation of the model postulates. The findings and discussions 

sequel to the analyses are discussed in the next section. 

 

RESULTS AND DISCUSSIONS 
 
Socio economic characteristics of the cocoa 

farmers Age of the cocoa farmers 
 
The age distribution of the cocoa farmers showed that 
none of the farmers was less than 28 years of age. Over 
68% of the farmers interviewed were over 50 years of 
age while the overall mean, mode and median were 55.8,  
65 and 58 years respectively. The F-statistic of 2.285 
obtained from the ANOVA (against the critical value of 
2.37) indicated that the differences in the mean ages 
across the six LGAs sampled were not statistically 
significant at 5% level.  

The implications of these findings were that most of the 
farmers were getting too old and may not be able to meet 
the demands which the intensive care of cocoa farms re-
quire. In addition young and energetic people were scar-
ce in the industry. This may lead to shortage of cocoa 
farmers in the near future. 

  
  

 
 

 

Present size of cocoa farms 
 
The size distribution of cocoa farms indicated that about 
65% of the cocoa farms were 2 hectares or less. Just 
about 5% had 5 hectares or more. The overall average 
size of holdings was 2.19 hectares. The mode and 
median were 1.54 and 1.89 hectares respectively. There 
were variations in mean farm sizes across the Local 
Governments Areas sampled as the computed F-statistic 
of 3.28 against the critical value of 2.37 indicated that at 
least two of the mean farm sizes have their differences 
statistically significant at 5% level.  

The conclusion that can be drawn from this finding is 
that cocoa production takes place on smallholdings. Thus 
a representative cocoa farmer in southwestern Nigeria is 
a small-scale producer. This further shows that despite 
the SAP measures, cocoa production had not shown a 
remarkable deviation from the pre-SAP days findings by 
Helleiner (1966) and Idowu (1986). 

 

Age of cocoa plantations 
 
The age distribution of cocoa plantations indicated that 
over 70% of the cocoa trees were above 30 years of age 
with less than 10% below the age of 20 years. The overall 
mean age of the plantation was 35.23 years while the 
modal and median values were 43.25 and 43.45 years 
respectively. 

The inferences from these findings are that: first, most 
of the plantations have exceeded their economic use life, 
generally taken to be 30 years (Oshikanlu, 1982). Se-
cond, relatively new plantations were not adequate 
enough to effectively replace the ageing ones. Third, it 
could be argued from the distribution that there was no 
mass replanting exercises during the 1980’s and 1990’s. 

 

Volume of cocoa produced per farmer 
 
The distribution of the volume of cocoa produced per 
farmer indicated that about 69% of the farmers produced 
less than one tonne of dried cocoa beans for the period of 
2005 cropping season. Only about 9% produced above 
two tonnes for the same period. The overall mean output 
per farmer was 0.973 tonnes while the mode and median 
values were 0.712 and 0.796 tonnes respectively. The 
computed F-ratio of 5.7228 (against the critical value of 
2.37) showed that there were variations in output per 
farmer across the Local Government Areas. This further 
buttresses the fact that the representative cocoa farmer in 
the southwestern Nigeria is a small-scale producer. 

 

Types of access gained to cocoa farms cultivated 
 
The system of acquiring access to cocoa farms tends to 
follow three main patterns viz:- (i) inheritance (ii) operator 

cultivated, and (iii) leasing Most of the times, the farmers 

possess a combination of different access types. It was 



 
 
 

 
Table 1. Estimated structural equations of cocoa production at farm level dependant variable Qo = output per 

farmer (kg). 
 

 LGA'S  CONSTANT X1 X2 R
2
 Adj R

2
 F-ratio DW 

 

 EGBEDA         
 

 Linear   198.57 19.81000* -9.2500* 0.8598 0.84646 64.400 1.56 
 

          (62.33) (1.74000)      
 

 Double  1.47000 1.00000* -0.10700 0.8963 0.8865 90.841 1.74 
 

 Log  (0.12843) (0.07458) (0.08591)    8 
 

 ISOKAN          
 

 Linear  -112.6800 31.07700* -0.79340* 0.9513 0.94557 166.039 2.67 
 

          (75.2000) (0.07458) (0.08591)     
 

 Double  1.42700 1.05112* -0.08409 0.90855 0.84779 84.446 2.28 
 

 Log  (0.15200) (0.08313) (0.07392)     
 

 AYEDAADE         
 

 Linear   90.59000 27.55800* -8.52200 0.979 0.957 54.329 2.49 
 

          (106.950) (1.22000) (9.02080)     
 

 Double  1.43340 1.07500* -0.12100 0.97018 0.94126 168.240 2.07 
 

 Log  (0.12170) (0.05863 (0.09174)     
 

 IREWOLE          
 

 Linear  9.52587 25.40090* 1.25036 0.8514 0.84147 85.925 2.40 
 

          (65.9260) (1.998) (2.12070)     
 

 Double  1.45847 0.97100* -5.67000* 0.79463 0.78094 58.040 2.19 
 

 Log  (0.14834) (0.09419) (0.10488)     
 

 ILE OLUJI          
 

 Linear  23.84289 (98.4200) 21.37378* 2.78650 0.9484 0.94438 238.709 2.11 
 

           (1.04305) (5.95195)     
 

 Double  1.39346 0.94648* 1.04483 0.95864 0.95546 301.304 2.07 
 

 Log  (0.07357) (0.04399) (0.06135)     
 

 OWENA         
 

             

 Linear  -58.76600 25.37815* -940975 0.80149 0.77813 34.318 2.39 
 

          (249.387) (3.06682) (13.366)     
 

 Double  1.24029 1.08700* -0.05426 0.8994 0.88754 75.973 2.77 
 

 Log  (0.15764) (0.8977) (0.07348)     
 

 ALL LOCAL         
 

 GOVTS .  761.2820 23.18600* -1.21698 0.8935 0.89207 616.771 2.11  

  
 

 Linear  (39.0874) (0.66334) 1.89235     
 

 Double  1.45995 0.98335* -0.04302 0.89646 089505 636.380 2.11 
 

 Log  (0.05017) (0.02778) (03351)     
 

                 
  

Figures in parenthesis are the standard errors.*Indicates the significant variables at 5% level. 
 
 

 

found that about 30% of the farms were inherited; the 
operators cultivated 38% while just 10% obtain their ac 
cess rights through leasing. About 18.70% of the farms 
comprised of inheritance and operator operated.  

The existence of a large proportion of inherited farms 
could explain the ageing condition of most of the planta-
tions. This can pose a serious bottleneck to inflow of cre-
dits to the industry from the formal sources, as the len-
ders may not be willing to accept ageing and inherited co-
coa farms as collateral. 

 
 
 
 
Levels of education of the farmers 
 
The amount of effort put into any economic activity, the 
risk bearing and the readiness to adopt new farming 
technology depend on age and literacy levels (Alimi and 
Awoyomi, 1995). This assertion makes education of 
cocoa farmers an important variable.  
It was found that 34% of the respondents did not have 

any formal education; another 14% had adult education 

while 30% had just primary education. Only 21% attend- 



  
 
 

 
Table 2. Cocoa response functions for the region. 

 

 Linear Function   Double-log Function 
 

Variables 
 

Coefficients 
 

T-value Variables 
 

Coefficients 
 

T-value 
 

    
 

Qs     Log Qs     
 

Xa  0.0188  2.025* Log Xa  0.4262  2.376* 
 

Xb  5.549  2.179* Log Xb  -0.2290  0.949 
 

Xc  0.00079  1.284 Log Xc  0.4728  3.160* 
 

Xd  0.00019  2.530* Log Xd  0.1935  0.633 
 

Xe  -1.488  -0.573 Log Xe  -0.1020  -0.800 
 

Constant  -41.7  -0.566 Constant  0.0714  0.018 
 

R2 0.923613   R2 0.94324   
 

Adj. R
2
 0.79613   Adj. R

2
 0.8723   

 

F-ratio 7.244   F-ratio 13.29   
 

D-W Test 2.194   D-W Test 2.49   
  

*Significant at 5% level 
 

 

ed secondary school and other institutions of higher 

learning. This implied that literacy level is very low among 

the cocoa farmers of southwestern Nigeria 
 
 

Factors influencing cocoa output at the farm level 
 
Table 1 presents the empirical results obtained for the 
cocoa output per farmer for each Local Government Area 
(LGA) and all the LGAs combined. Judging by the 

coefficient of multiple determination (R
2
), and the F-

values obtained, all the structural equations demonstra-

ted good fits. However only variable X1 (mandays of 
labour) conformed to the a priori expectation for all the 

equations. Variable X2 (intensity of chemical used) had 
negative regression coefficients in contrast to the a priori 
signs except for Irewole and Ile Oluji Local Government 

Areas. Furthermore, only X1 was statistically significant in 

all, while X2 was only significant in Egbeda and Ile Oluji 
Local Government Areas.  

With respect to the result for Egbeda Local Govern-

ment Area, the independent variables X1 and X2 explain-

ed about 85% of the adjusted variabilities in the volume of 
cocoa produced per farmer. For example an increase of 
one manday in the number of labour used will lead to an 
increase of about 19.8 kg of cocoa produced while an 
increase of 1 kg/ha of chemical used will lead to a 
decrease of about 9.25 kg of cocoa produced per farmer, 
ceteris paribus. Similar interpretation goes for all other 
LGAs. It was observed on the field that the larger the 
number of plots or sizes of cocoa farms cultivated, the 
fewer the number and quantity of fumigations applied 
(chemical use on cocoa trees). Hence the smaller the 
quantity of chemicals applied per hectare the larger the 
size of the farm. This implied that farmers with smaller 
plots utilize more quantity of chemicals per hectare than 
those with large farm sizes. Thus, the unexpected nega-

tive regression coefficient on X2. 

 
 

 

Estimated cocoa production response functions for 

the region 
 
cocoa production response function earlier specified. The 
in equation (2) was estimated using the least square 
regression techniques. Both the linear and double-log 
functions were estimated and the empirical results are 
presented in Table 2.  

The results indicated that the explanatory power of the 
linear model is about 92% while that of the double-log is 
about 94%. Judging by the value of their F-ratios, the two 
equations demonstrated good fits. Also the Durbin 
Watson test for the two functional forms (D-W Test = 2.19 
and 2.49) showed that there was no serious autocorre-
lation among the variables. All the variables in the linear 
model conformed to their economic a priori signs. The 
significant variables are the lagged real producer prices 

(Xa), lagged exchange rate (Xb) and the aggregate 

fertilizer supply for the year (Xd).  
Using the coefficients of the double-log function as the 

measure of the direct elasticities, the result indicated that 
the short-run elasticity for the real producer price is 
0.4262. Other elasticities are lagged world price (0.4728); 
lagged exchange rate (-0.229); aggregate fertilizer supply 
(0.1935) and lending rate (-0.102).  

From the above, it can be suggested that significant 
increases in aggregate cocoa output in southwestern 

Nigeria can be achieved through a combination of sus-
tained increase in real producer and world prices; and an 

increase in aggregate fertilizer supply. 

 

Conclusion 
 

The internal and external imbalances created as a result 

of price distortions of the 1980s prompted the introduction 

of a structural adjustment programme in Nigeria with one 
of its major policy objectives being the market liberaliza- 



 
 
 

 

tion of agricultural exports. The paper examined the pre-
sent situation in the Nigerian cocoa industry and con-
cludes that despite about two decades of operating SAP 
and economic liberalization in the country, the cocoa 
industry still remains under smallholder production.  

However, the study concluded that aggregate cocoa 
output could be stimulated through a combination of a 
number of factors influencing the cocoa industry. These 
are sustained increase in real producer and world prices; 
and an increase in aggregate fertilizer supply. In addition, 
all the stake holders should carry out mass replanting 
exercises to replace old stocks of cocoa trees on 
plantations along with provision of incentives that will 
encourage young people to invest in cocoa farming. The 
current efforts of the Federal and State Governments 
aimed at replanting ageing cacao trees (called Cocoa 
Rebirth Programme) should be supported by all the 
stakeholders as a major way of boosting and developing 
the Nigerian cocoa industry. 

 
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