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Deutscher Tropentag 2005 – Hohenheim, October 11 - 13, 2005
Conference on International Research on Food Security, Natural Resource Management and Rural
Development
Quality Management Practices in Cocoa Production in South - Western Nigeria
Opeyemi Anthony Amusan1, Fidelis Olumide Amusan2, Ademola Braimoh3, Philip Oguntunde4:
1
University of Bonn, Agricultural Sciences & Resource Management in the Tropics and Sub-Tropics (ARTS),
Institute of Agriculture Water Engineering, Nussallee 1, 53115 Bonn, Germany [email protected],
[email protected]
2
Alcon Labs, Clinical Research Scientist, United Kingdom
[email protected], [email protected]
3
United Nations Institute of Advance Studies (UNU-IAS), Japan
[email protected]
4
University of Technology Delft, Water Management, The Netherlands
[email protected]
Abstract
The main objective of this study was to investigate quality management practices in the major cocoa production
areas of Nigeria. Socio - economic surveys covered resource quality, agronomic practices, and constraints to
agricultural production, whereas soil sampling and analyses were carried out to assess contribution of soil to
yield. Farm budget analysis was used to determine the profitability of the two major management options of
sampled farmers. Linear multiple regression was used to relate biophysical and agronomic data to cocoa yield.
Owing to high level of multicollinearity, independent variables were reduced to six, which are Organic C, Age of
farm, Plant density, Proportion Replaced, Crop Variety and ECEC. Among the variables in the model, two
(Organic C and Age of farm) are negatively related to cocoa yield, whereas other variables are positively related
to cocoa yield. However, soil variables are not significant to the model (p>0.05), whereas three management
variables (plant density, proportion of dormant plants replaced and crop variety) are significant (p<0.1). All the
variables explain 97% of the variability of yield and the model can be used to predict yield at 99% confidence
level. Results indicated variability in yield across the three main locations studied. The highest yields were
obtained in areas where farmers have access to training in management practices. Soils of the three locations are
not significantly different from one another in terms of chemical properties. This probably reflects similarity in the
parent materials from which the soils have developed. Farm budget analysis revealed that minimal management
involving fertilizer and pesticide use is less profitable than extensive management. For sustainable cocoa
production in the study areas, a high premium should be placed on the quality of cocoa product for export.
2. Backgrounds and Aim of the Study
One of the many factors responsible for the decline in cocoa production in South-western Nigeria is the ageing
cocoa farms. Many farms are over 40 years old and such farms constitute as much as 60% of the cocoa farms in
the country today (Adegeye, 1997). Orderly marketing of cocoa also almost stopped with the abolition of the
Nigerian Cocoa Marketing Board (NCB) (Ajayi and Okoruwa, 1996). The immediate effect was the bad quality
cocoa that was leaving Nigeria for Europe and hence loss of premium price which had been placed on Nigerian
cocoa.
This study is aimed at assessing the quality management practices in cocoa production in the study areas in
south - western Nigeria. The specific objectives include:
1.
2.
3.
To highlight which management practices affect the overall quality in the cocoa production chain among
the cocoa growers in Nigeria,
To determine the biophysical and socio-economic constraints to cocoa production, and
To assess possible techniques for improved management practices for higher yield and profitability for
cocoa farmers.
The study examines farm units in the three cocoa producing centres in Nigeria in respect of their agro management practices. Empirical analyses of the farm units is intended to provide insights into available options
in optimising management practices for cocoa cultivation and show the resulting effect on producer incomes. In
general, the study will determine what pre-requisites must be fulfilled in the pursuance of good management
practices aimed at improving farmers´ income through efficient production processes, and realising full and
sustainable production potential of the farm holdings.
3. Methods
The methodology adopted for this study falls under three broad but integrated categories. First, socio-economy
survey was carried out to elicit information on farm-farmer characteristics vis-à-vis constraints to cocoa
production. Secondly, soil sampling and analysis were carried out to examine the role of soil properties in crop
yield. Socio-economic and soil data were thereafter integrated to study their influence on crop yield. Lastly,
financial analysis comprising project costing and investment projections for two management levels of cocoa
production was carried out to highlight profitability.
3.1
Socioeconomic Survey
A survey was carried out among cocoa growers in South-western Nigeria in June and July 2002. Three locations
having similar agro-ecological features were selected, namely Ibadan, Ife and Akure. Thirty cocoa farmers were
randomly selected and interviewed on their farms using standardised questionnaires. Farmers interviewed in
Ibadan belonged to three different groups of cooperative societies or farmers´ organisation. The farmers
interviewed at Ife and Akure belonged to the respective cooperative multipurpose unions (CPMU) in these towns.
These cooperatives are profit -making organisations, however, they offer more than just marketing services for
their members. Access to the farmers and their farmlands was obtained under the auspices of the Association of
Nigerian Cooperative Exporters Limited (ANCE) and ongoing Project for Improvement of Cocoa Marketing and
Trade in Nigeria (ICMT) sponsored by the International Cocoa Organisation (ICCO). While cocoa farmers in Ife
and Akure are fully integrated into the ICMT project, the cocoa farmers in Ibadan are non-participants. The
selection along the line of participation or non-participation on the project moreover enabled the evaluation of the
project’s overall influence on cocoa yield. The interview comprised of qualitative and quantitative components,
covering aspects of agronomic activities, resource quality and availability, problems perceived and objectives set
both at individual and cooperative levels. Additional information was collected in key-persons interviews from
cocoa exporters, cocoa processors, cocoa researchers and representatives of governmental and nongovernmental institutions. Official statistics and other secondary data served as background information.
3.2
Soil Sampling and Analyses
Biophysical data was obtained by analysis of soil samples taken from cocoa farms. Soils were analysed for
chemical analyses. The soils were analysed for basic cations (determined in 1N NH4OAc), total N (Kjedahl
method), available P (Bray P method) organic C (Walkey-Black wet oxidation method) and pH (0.1M CaCl2).
3.3
Farm Budget Analysis
The objective of the farm budget analysis was to determine strategies for improving profitability of cocoa
production. Thus analysis was carried out for two different management levels. The first is ‘minimal management
level’ involving the use of farms inputs such as fertilizer, pesticides etc at a reduced level. The second is
‘extensive management level’ where fertilizer and pesticide application is not involved. An illustrative financial
model based on the study findings was prepared for 1 ha land for cocoa production, each at the two management
levels to show estimate of expenditure and expected gross margins.
4. Results
4.1 Soil Properties, Land Use History and Crop Yield
The topography of the land at all study farms ranges from flat to gentle slope (2-8%). Soil analyses (Tables 3 and
4) showed that the soils are fairly high in total N (mean = 0.20%), organic C (mean = 1.89%), available P (mean =
3.19 ppm), but low in Na. The pH is close to neutral (mean 6.5). In terms of variability, pH is the least variable (cv
= 7%) while available P is the most variable property (cv = 69%). Statistics of soil properties at the sampled
locations are presented in Tables 3 and 4. Soil pH is the least variable across locations (CV=3-9%). Highest CV
(100%) among Ibadan soils occur for EA, while the highest CV of 119% occur for P for Ife soils while Ca and K
manifests the most variable soil property amongst Akure soils (C=55%). This suggests that cations are the most
variable properties among Akure soils.
Table 1
Estimates for Maintenance Labour required per Hectare for two Cocoa Management
Systems (Source: own computation)
Management
System
Type of practice
Man-days/ha/year
Extensive
pod harvesting and breaking
fermenting and drying
manual weed control; pruning
Total
5
5
11
21
Minimal
regular pod harvesting and breaking
fermenting and drying
regular manual weed control
structural pruning
disease / pest control (chemical) / fertilizer
Total
10
10
10
4
7
41
Table 2: General Statistics of soil properties (N=16) –(Source: own soil data analyses)
EA= Exchangeable Acidity, ECEC= Effective cation exchange capacity
Mean
Std
Cv
(%)
min
max
Ca
(cmol/kg)
Mg
(cmol/kg)
Na
(cmol/kg)
K
(cmol/kg)
EA a
(cmol/kg)
ECEC
(cmol/kg)
a
4.34
2.08
48
1.11
0.48
43
0.07
0.02
29
0.38
0.21
25
0.02
0.02
100
1.49
7.51
0.30
1.22
0.04
0.10
0.06
0.76
0.00
0.07
5.90
2.55
43
Total
N
(%)
0.20
0.05
27
2.15
9.77
0.14
0.33
Available
P (ppm)
3.19
2.19
69
Organic
C
(%)
1.89
0.61
32
pH
6.46
0.44
7
0.99
7.05
1.16
3.33
5.70
7.30
Table 3: Location Statistics of soil properties (N=16) – (Source: own soil data analyses)
Location
No of Statistics Ca
samples
Ibadan
8
Ife
4
Akure
4
Mean
Std. dev
CV (%)
Mean
Std. dev
CV (%)
Mean
Std. dev
CV (%)
Mg
4.46
2.06
46
3.89
2.37
61
4.73
2.61
55
Na
1.09
0.44
40
0.97
0.47
48
1.31
0.64
49
K
0.07
0.02
29
0.06
0.01
17
0.08
0.02
25
EA
0.33
0.23
70
0.42
0.12
29
0.44
0.24
55
ECEC N
0.02
0.02
100
0.02
0.01
50
0.02
0.01
50
5.96
2.39
40
5.36
2.74
51
6.58
3.44
52
P
0.19
0.05
26
0.21
0.08
38
0.2
0.04
20
OrgC
3.48
2.53
73
5.5
6.56
119
2.33
0.93
40
1.84
0.57
31
2.11
0.88
42
1.79
0.45
25
pH
6.45
0.49
8
6.56
0.57
9
6.38
0.22
3
4.2 Effects of biophysical and management variables on crop yield
Linear multiple regression was used to relate crop yield to biophysical and socio-economic data. Owing to high
level of multicollinearity, independent variables were reduced to six.
Relative importance of variables in the multiple regression model
Source: own computation
0.6
0.5
s
ta
n
d
a
rd
ize
db
e
ta
Figure 1
0.4
0.3
0.2
0.1
0
-0.1
Organic C
Age of
farm
Plant
density
Proportion
Replaced
Crop
Variety
ECEC
Table 4
Multiple regression of yield versus soil and management variables
Source: own computation
Independent variables
Organic C
Age of farm
Plant density
Proportion Replaced
Crop Variety
ECEC
Squared Multiple R
Standardized β
-0.05
-0.07
0.17
0.48
0.42
0.02
t
-0.55
-0.95
2.00
4.05
3.32
0.20
Probability
0.59
0.37
0.08
<0.01
0.01
0.85
0.97
F Probability
<0.01
Among the variables in the model, two (Organic C and Age of farm) are negatively related to cocoa yield, whereas
other variables are positively related to cocoa yield. However, soil variables are not significant to the model
(p>0.05), whereas three management variables (plant density, proportion of dormant plants replaced and crop
variety) are significant (p<0.1). All the variables explain 97% of the variability of yield and the model can be used
to predict yield at 99% confidence level. Figure 2 further shows the relative importance of the variables as
measured by the standardized β coefficients. Proportion of dormant plants replaced is the most significant
management variable affecting yield, followed by crop variety (that is, F3 Amazon).
4.3 Farm Expenditure and Gross Margin
A summary of farm budget under ‘minimal’ cocoa holding is presented in Table 5.
Table 5 Summary of budget under ‘minimal’ cocoa holding in 2001/02-(Source: own C)
Yield (kg/ha)
Price =N=/kg
TOTAL REVENUE (/ha)
INPUTS (/ha)
Seedlings (replacement of dormant
trees)
Pesticide: Gammalin 20
Fungicide: Perenox
Fertilizer: NPK
450
110
Hired Labour
cleaning and cultivation
planting and pruning
weeding and spraying
harvesting and processing
Bags and Packaging material
Estimated 41 man days/ha
@ 400 Naira/day for 1 ha to
cover a cocoa season
16400
About 3600 Naira for a
cocoa season
3600
TOTAL INPUT COST (A)
FIXED EXPENSES
Leased Land
Depreciation of equipment
TOTAL FIXED COSTS (B)
TOTAL COSTS (A) + (B)
GROSS PROFIT
=N=
=N=
=N=
49500
Costs (Naira)
12 Naira @ 115
seedlings/ha
800 Naira @ 3 liters/ha
875 Naira @ 4 liters/ha
2200 Naira @ 1 bag/ha
Total
1400
3200
3500
2200
30300
10% of Total Revenue
2000
4950
6950
47250
2250
The table shows that total fixed cost is N6950, while the total variable cost per unit
30300
= 67 Naira . The model shows that, at this management
450
6950
= 210kg and provides a gross profit of 2250 Naira per ha.
(110 − 67)
is
level, the farm breaks-even at
A summary of farm budget under ‘extensive’ cocoa holding is presented in Table 6.
Table 6: Summary of budget under ‘extensive’ cocoa holding in 2001/02-(Source: own C)
Yield (kg/ha)
Price =N=/kg
TOTAL REVENUE (/ha)
INPUTS (/ha)
Seedlings (replacement of dormant
trees)
Hired Labour
cleaning and weeding
planting and pruning
harvesting and processing
Bags and Packaging material
TOTAL INPUT COST (A)
FIXED EXPENSES
Leased Land
Depreciation of equipment
TOTAL FIXED COSTS (B)
TOTAL COSTS (A) + (B)
GROSS PROFIT
230
110
=N=
=N=
=N=
25300
Costs (Naira)
12 Naira @ 115 seedlings/ha
1400
Estimated 21 man-days/ha @
400 Naira/day for 1 ha to
cover a cocoa season
8400
About 1800 Naira for a cocoa
season
1800
11600
10% of Total Revenue
2000
2500
4500
16100
9200
The Table 6 shows that total fixed cost is N4500, while the total variable cost per unit is
The model shows that, at this management level, the farm breaks-even at
11600
= 50 Naira .
230
4500
= 75kg
(110 − 50)
and provides a
gross profit of 9220 Naira per ha.
Comparison of Tables 5 and 6 shows that minimal and extensive management levels result in a gross profit of
2250 Naira and 9200 Naira respectively. Although extensive management generates lower revenue compared to
minimal management, gross profit is greater as the input costs are not as high.
An estimation of gross margin for the average cocoa farm per hectare for the periods before this study also
confirms that a general decrease in input costs improved the profitability ratio (Table 7).
Table 7
Gross Margin for the Average Cocoa Farm per Hectare
Source: Computed from field survey data (2000)
Average farm size (ha)
REVENUE
Yield (tonnes/ha)
Price (=N=/tonnes)
Total revenue
VARIABLE COST
Pesticide
Fertilizer
Herbicide
Labour
Total Variable Cost
Gross Marginal/ha
Profitability ratio (GM/TC)
1985/86
1992/93
1999/2000
4.35
4.50
4.58
0.503
20,000
10,060
0.544
94,660.00
51,495.04
0.579
91,340.00
52,885.86
449.86
35.45
48.0
3,550.00
4,083.00
5,976.6
1.46
506.25
3.10
0.00
7,640.00
8,149.4
43,346.00
5.67
582.10
0.00
13.00
11,400
11,995
40,890.8
3.41
Input cost (cost of pesticide, fertilizer and herbicide) in 1985/86 was =N= 533.31 whereas the profitability ratio in
this season was 1.46. In 1992/93, input cost was =N= 509.35 and profitability ratio was 5.67. In 1999/200, input
cost was =N= 595.1 and profitability ratio was 3.41.
5. Conclusion and Recommendations
This study shows that high yield in cocoa farming does not necessarily imply high profitability. Since farms, which
do not apply chemical input, have been able to maintain some level of profitability, extensive farmers should be
encouraged to try to get a higher price for their product, which could actually be called ‘organic cocoa’. Farmers,
who apply chemical inputs, must consider the aspect of profit making and quality of cocoa due to chemical
residues in the product, coupled with the problem of environmental pollution through chemicals.
Farmers should be encouraged to practise conservation and rehabilitation agriculture. This involves all steps
which negate the processes of degeneration on cocoa farms, for example through consequent replacement of
dormant trees, control of tree population for effective ground cover, properly managed canopy, integrated
management systems that utilise resistant cultivars and application practices that emphasise proper timing and
effective use of fertilizers at lower application rates. This would help not only to improve yield but also has the
advantages of profitability, product quality and environmental protection. Given the situation in the international
market in and the natural limitations of the cocoa farmers in Nigeria, the development of a market niche for quality
was suggested.
This research highlighted the management practices, which affect the overall quality in the cocoa production
chain on the visited farms. It revealed the biophysical and socio-economic constraints to cocoa production. The
suggested improved management practices will help cocoa farmers to realise higher yield and improve the
profitability of cocoa farming.
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