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Investment in cocoa production in Nigeria: a cost and return analysis of three cocoa production management systems in the Cross River State cocoa belt.

INTRODUCTION

The Nigerian cocoa economy has a rich history which is well documented in literature. The contributions of cocoa to the nation's economic development are vast and have been reported by many authors [1, 2, 3]. In terms of foreign exchange earnings, no single agricultural export commodity has earned more than cocoa. With respect to employment, the cocoa sub-sector still offers quite a sizeable number of people employment, both directly and indirectly [4, 5]. In addition, it is an important source of raw materials, as well as source of revenue to governments of cocoa producing states.

Because of its importance, the recent Federal Government's concern of diversifying the export base of the nation has placed cocoa in the centre-stage as the most important export tree crop. Evidence has however shown that the growth rate of cocoa production has been declining, which has given rise to a fall in the fortunes of the sub-sector among other reasons [6]. Folayan, Daramola and Oguntade (2006), note that cocoa production in Nigeria witnessed a downward trend after 1971 season, when its export declined to 216,000 metric tons in 1976, and 150,000 metric tons in 1986, therefore reducing the country's market share to about 6% and to fifth largest producer to date. In fact, the recent cocoa stakeholders forum held in Calabar, Nigeria by the Presidential Initiative on cocoa was to deliberate on the state of the cocoa sub-sector and reach consensus on how investments in the cocoa sub-sector can be strengthened and increased among other issues that bother on the sub-sector, in view of the Government's renewed interest to boost cocoa production, domestic utilisation and export.

Prior to the Structural Adjustment Programme (SAP), cocoa marketing was carried out by the erstwhile highly regulated Commodity Marketing Boards, which were known to pay farmers far less than the export price of cocoa. This situation affected cocoa production and export in the past as it served as a disincentive to investment in cocoa production. Even after the abolition of the Marketing Boards structure, cocoa production has still not fared better as is evident in the declining production trend reported in previous studies. One of the possible reasons for this may be the nature of investment in cocoa production, as some worry has been expressed as to whether the returns from cocoa are not being threatened by such factors as rising costs of production, price instability, and differences in management systems and perhaps declining productivity due to ageing trees. Generally, if investment in cocoa production were attractive, farmers/investors would allocate scarce resources to cocoa farming. However, the problem is that most individual investors and even governments have only a vague idea of the potential of the industry and as such are sometimes slow in committing investment funds into the sub-sector. Beyond this, information on how the different management systems affect costs and returns has scarcely been documented. Thus, this study empirically investigates costs and returns from different cocoa production/management systems in Cross River State cocoa belt with a view to provide some informed basis for investments in the sub-sector, and particularly a guide as to which management has the highest return, and hence would raise earnings from investment in cocoa for the producers as well as exporters.

From the empirical standpoint, the key questions which need to be addressed are: What are the key socioeconomic characteristics of cocoa farmers in Cross River State? What are the various management systems in operation in the study area? What are the net present values, and benefit-cost ratios of the various management systems? Which of the management systems is more economically viable?

The sequence of this paper is as follows: the section which follows presents the methodology comprising the analytical framework, models specification and the data. Section 3 presents and discusses the results of the empirical exercise, while the last section summarises the study and concludes with policy implications.

METHODS

Analytical Framework

The analytical framework comprises both univariate descriptive statistical techniques and an investment decision model. Cocoa farmers' characteristics (such as age, educational attainment, farm size, sources of funds, etc) were examined using descriptive statistics, while an investment decision model employing the use of the Net Present Value (NPV) and Benefit-Cost Ratio (BCR) was deployed to determine the most economically viable of the three management systems of cocoa production identified in the State, namely, owner-managed, lease-managed, and share-crop managed systems.

The Investment Decision Model

Net Present Value (NPV)

The net present value can be used as an important tool in making a decision by an investor to invest in cocoa production. Benefits and costs are linked to the age of the trees. At the early stages, there are heavy costs which are then followed by annual benefits that continue over the full life of the trees once they have reached maturity. Thus, following Gotsch and Burger (2001), if we define [INC.sub.it] as the net income (or benefit or return) from i-year-old trees as expected in year t, then the net present value of the expected net income from one hectare of cocoa in year t for one cycle of I years duration amounts to:

[NPV.sub.I,t] = [I.summation over (i=0)] [INC.sub.i,t]/[(1 + r).sup.i] (1)

Meanwhile, the expected net income per hectare in year t is given as:

[INC.sub.t] = [I.summation over (t=1)] [REV.sub.i,t] - [TC.sub.it]) (2)

Where

[REV.sub.i,t] = the expected revenue per hectare from i-year-old trees in year t;

[TC.sub.i,t] = the total cost per hectare from i-year-old trees in year t;

r = the discount rate or the opportunity cost of capital; and

t = the time period.

The formal selection criterion for the net present value is to accept investments with net present value greater than zero. However, if the net present value works out to be negative, then we have a case in which, at the chosen discount rate, the present worth of the income or benefit stream is less than the present value of the cost stream. Hence the revenues are insufficient to allow for the recovery of the investment. An investment is technically and economically feasible if the net present value is positive.

Benefit-Cost Ratio (BCR)

The Investment Decision Model also utilizes the Benefit-Cost Ratio, which is another indicator of the worthiness of an investment decision. It is given as the ratio of the sum of discounted benefits to the sum of discounted costs. Thus, for a cycle of I years duration, the benefit-cost ratio can be represented by the formula:

[BCR.sub.I,t] = [I.summation over (i=0)] [DREV.sub.i,t]/[DTC.sub.i,t] (3)

Where:

[DREV.sub.i,t] = discounted revenue (benefits) per hectare from i-year-old trees in year t;

[DTC.sub.i,t] = discounted total costs per hectare from i-year-old trees in year t;

The decision rule is that for any project to be economically viable, the ratio must be greater than unity [9].

Sampling Procedure, Data and Implementation Techniques

The study area is Cross River State, Nigeria. A two stage sampling procedure was adopted in this study. The first stage involved the purposive selection of the two Local Government Areas known to be the largest cocoa producing areas in the State and which form the State's cocoa belt, that is Ikom and Etung Local Government Areas. The second stage involved the random selection of 50 farmers apiece from the three management systems of cocoa production (a total of 150 respondents) identified in the study area based on a sampling frame constructed to identify key cocoa farmers in the area. A structured survey instrument was used to obtain the information utilised in the study. The data from the questionnaire was augmented with secondary information from the respondents who kept records, and with data from the Cross River State Ministry of Commerce and Industry, Ministry of Agriculture, Planning, Research and Statistics, the Central Bank of Nigeria (CBN), as well as United Nations Environmental Programme (UNEP).

For the cross-sectional survey of the respondents which took place in 2002, cocoa output was measured in bags of 64kg or 0.064 tons. Average cocoa price at the period was N8,864 per bag; that is N138,500 per ton; labour cost per man-day was put at N200. Age was measured in years and represented how old the farmer was at the time of his study. The per hectare establishment costs, maintenance costs before maturity were obtained from the Ministry of Agriculture. Straight line depreciation method was used to get the actual value of the fixed cost of the assets during the 2002 production season. A discount rate of 10% was used to represent the interest rate or the opportunity cost of capital. The justification for the choice of 10% is because of the preferred rates of interest for agricultural investments, which are always lower than the market rates of interest [10].

Since one of the major changes in tree stock occur due to time, that is as the trees grow older, they first become more and later less productive, a time horizon of thirty years which approximates the expected life of a cocoa tree was used in the investment decision analysis checking for differences across the management systems. Thus, the yield profile of cocoa trees in Nigeria with respect to age of tree and year of planting was obtained from UNEP in Nigeria, and used to project the yield of trees thirty years back, based on the observed 2002 yield. Similarly, projections were made for cocoa prices based on 2002 cocoa price in Naira per ton following the growth rate of cocoa producer prices reported for Nigeria by the FAO. This also applied to the per hectare costs of maintenance from maturity obtained from UNEP.

These values were then used in estimating NPV and BCR for the various management systems with the assumption that differences would only be due to how the various systems were run.

RESULTS

Socioeconomic characteristics of cocoa farmers Age composition and educational level

Table 1 shows a summary of the socioeconomic characteristics of the respondents. On average, the owners are the oldest group of farmers and the lease-managers the youngest, with share-croppers being intermediate. The sharecroppers have the lowest education on average and the lease-managers the highest.

Farm size

The farm size distribution of the respondents reveals that under the three management systems, majority of the plots ranged between 1 and 5 hectares. Moreover, 28% of plots under owner-managers fall within the 6-10 hectare bracket, while it was 10% for lease-managed systems and 4% for sharecrop systems. These results hint that cocoa farm owners reduce risks by leasing out their farms in rather small units than giving out very big units to a single lease manager or sharecropper.

Sources of funds

Results indicate that majority of the respondents in the three management systems funded their production activities from personal savings. Particularly, 6% of the owner-managers and 12% of the lease-managers obtained bank loans while share croppers did not obtain funds from any formal credit source. On the other hand, more farmers under the sharecropping system obtained funds from relations compared with the other two systems.

Marketing channels

Of the two marketing channels identified, one is from the producer to the licensed buying agent (LBA), the merchant and finally exports, while the other is from the producer to the small-scale buyer, the licensed buying agent, the merchant and then export. Table 1 shows that majority of the respondents from the three management systems taken together market their cocoa through the small scale buyers, who sell to the licensed buying agents, onto the merchants and finally to the export market, while the remainder pass through the licensed buying agent to merchant to the export market. This may be due to the fact that most of the farmers do not produce enough individually to sell directly to the licensed buying or merchants.

Descriptive statistics of costs and returns

Some descriptive statistics of costs and returns for the three management systems are presented in table 2. Lease-managed cocoa farms have a larger mean costs and returns per hectare followed by owner-managed farms. Standard deviations show that costs of owner-managed farms and sharecrop-managed farms are more clustered around the mean than lease-managed farms. Similarly, standard deviations also indicated that returns from the three management systems are widely dispersed from their means. The reason for the above structure, among others, may be the fact that the lease manager is primarily profit-motivated, unlike the sharecropper in this region, whose basic motivation is subsistence: the leaseholder needs a large outlay if he is to earn enough returns to cover lease and other costs and still make profit, whereas a sharecropper is a resource-poor worker, constraint by a lack of cash to own land/other inputs and cannot enjoy size economies beyond the limitations set by the landlord. A look at the sources of funds for the three systems (table 1) indicates the credit worthiness of lease managers: 12% of them have access to bank loans while no sharecropper had such access. The owner managers are just in between the two, combining both profit and subsistence motives at varying degrees.

Investment decision analysis Owner-managed farms

The benefit cost analysis for cocoa per hectare at 10% discount rate for owner-managed farms for a thirty-year period is shown in table 2. Results indicate positive NPV of N57,166.37 per hectare and estimated benefit-cost ratio of 4.27, which is greater than one. These results imply that owner-managed cocoa production systems are viable since they can pay for the factors of production and still make some profit.

Lease-managed farms

The results in table 4 above show that the calculated NPV is positive with a value of N6,9408.6 per hectare. This figure is higher than the calculated NPV for owner- managed farms. However, the benefit-cost ratio for leased-managed farms (4.04) is lower than 4.27 estimated for owner-managed farms. The results imply that lease-managed farms are more viable in terms of NPV than owner-managed farms.

Sharecrop-managed farms

The results indicate that the NPV for sharecrop managed farm is positive and estimated to be N28,956.83, while the benefit-cost ratio is 2.71. Although these results imply viability of the sharecrop managed systems in absolute terms, it is quite evident that it is the least viable relative to owner-managed and lease-managed systems. Obviously farmers only choose this option if they do not have the capital to own or lease land.

DISCUSSION

The study examined costs and returns in cocoa production in Cross River State in the context of three identified management systems of cocoa production in the area, namely owner-managed, lease-managed and sharecrop managed systems, using the hundred and fifty randomly selected cocoa farmers. Data were collected using structured questionnaires through the participatory approach using ADP extension agents as well as from secondary sources.

From the study, it can be inferred that majority of the cocoa farmers were in their prime ages. This may be due to the fact that cocoa production activities require physical energy and are labour intensive and thus require the young and energetic to be involved. Another important reason may be that since cocoa production is known to give relatively higher incomes than the other farming endeavours, it is the most likely farming activity that will attract young people. This was confirmed in a study by Amalu and Abang (1997).

Also, farmers' level of education in the study shows that education affects the nature in which farms are managed as well as their overall productivity, hence income. This is in line with economic theory. Accordingly, the viability of the various management systems may have been influenced by the level of education of the farmers. Furthermore, the analysis of farmers' sources of funds points to the fact that it is easier for owner-managers and lease-managers to obtain credit from formal sources than sharecroppers because they can provide what it takes to obtain such loans. Generally, the results show that access to bank loans by farmers is a big problem due to several reasons of which collateral and the risky nature of agricultural production are just but two.

Importantly, the investment analysis results show that cocoa production is a profitable business irrespective of management system, since all of them had positive NPV at 10% discount rate. The NPV for lease-managed farms is highest. The benefit-cost ratio at 10% discount rate was greater than one for the three management systems, which indicates that the returns from cocoa production are high. Owner-managed farms had the highest BCR followed by lease-managed farms in that order. Lease-managed farms were more viable compared with other management systems in terms of their high NPV.

CONCLUSION

The study recommends that given the high benefits relative to costs involved in cocoa production irrespective of management system, investments in cocoa production can be increased tremendously by providing expanded access to cheap and flexible credit and land, which have presented as limiting factors in cocoa production in the State based on the descriptive statistical analysis in the study.

REFERENCES

[1.] Olayide SO Some Estimates of Supply and Demand Elasticities for Selected Commodities in Nigeria's Foreign Trade. Journal of Business and Social Studies. 1969:1(9): 176-193.

[2.] Olayemi JK Some Economic Characteristics of Peasant Agriculture in the Cocoa Belt of Western Nigeria. Bulletin of Rural Economics and Sociology. 1973; 1: 24-30.

[3.] Folayan JA, Daramola GA and AE Oguntade Structure and Performance Evaluation of Cocoa Marketing Institutions in South-Western Nigeria: An Economic Analysis. Journal of Food, Agriculture and Environment. 2006; 4 (2): 123-128.

[4.] Abang SO Stabilization policy: An Economic Analysis and Evaluation of its Implication for Nigerian Cocoa Farmers. PhD Thesis, Oklahoma State University, Stillwater. 1984: 212.

[5.] Folayan JA, Daramola GA and AE Oguntade Structure and Performance Evaluation of Cocoa Marketing Institutions in South-Western Nigeria: An Economic Analysis. Journal of Food, Agriculture and Environment. 2006; 4 (2): 123-128.

[6.] Nkang NM, Abang SO, Akpan OE and KJ Offem Cointegration and Error Correction Modelling of Agricultural Export Trade in Nigeria: The case of Cocoa. Journal of Agriculture and Social Sciences; 2006; 2(4): 249-255.

[7.] Folayan JA, Daramola GA and AE Oguntade Structure and Performance Evaluation of Cocoa Marketing Institutions in South-Western Nigeria: An Economic Analysis. Journal of Food, Agriculture and Environment. 2006; 4 (2): 123-128.

[8.] Gotsch N and K Burger Dynamic Supply Response and Welfare Effects of Technological Change on Perennial Crops: The Case of Cocoa in Malaysia. American Journal of Agricultural Econonomics. 2001; 83(2): 272-285.

[9.] Gittinger JP Economic Analysis of Agricultural Projects. The John Hopkins University Press, London. 1989: 299-362.

[10.] Federal Ministry of Agriculture, Water Resources, and Rural Development Agricultural Policy for Nigeria. Directorate for Social Mobilization, MAMSER, Abuja, Nigeria. 1988; 1-65.

[11.] Amalu UC and SO Abang Survey and Constraint Analysis of Yam-based Cropping Practices in Two Rainforest communities of South-East Nigeria. Nig. South-East Journal of Agricultural Economics and Extension. 1997; 1(1): 19-22.

Nkang N M * (1), EA Ajah (2), SO Abang (3) and EO Edet (4)

* Corresponding author e-mail: nkangm@yahoo.com

(1) Lecturer, Department of Agricultural Economics and Extension, University of Calabar, P. M. B. 1115, Calabar - Nigeria.

(2) PhD Candidate, Department of Agricultural Economics and Extension, University of Calabar, P. M. B. 1115, Calabar - Nigeria. E-mail: agomajah95@yahoo.com

(3) Professor, Department of Agricultural Economics and Extension, University of Calabar, P. M. B. 1115, Calabar - Nigeria. E-mail: soabang@yahoo.com

(4) Assistant Lecturer, Department of Agricultural Economics and Extension, University of Calabar, P. M. B. 1115, Calabar - Nigeria. E-mail: eyoorok@yahoo.com
Table 1: Socioeconomic characteristics of cocoa farmers in Cross River
State.

Variables Owner-Managers Leased-Managers

 Frequency Percentage Frequency Percentage
Age
21-30 7 14 10 20
31-40 20 40 20 40
41-50 15 30 10 20
Above 50 8 16 10 20
Total 50 100 50 100

Educational
Level
No formal 10 20 4 8
education
Primary 13 26 10 20
Secondary 14 28 26 52
Tertiary 6 12 7 14
Others 7 14 3 6
Total 50 100 50 100

Farm Size
1-5 32 64 45 90
6-10 14 28 5 10
Above 10 4 8 0 0
Total 50 100 50 100

Sources of
funds
Personal Savings 38 76 37 74
Bank loans 3 6 6 12
Informal Loans 2 4 3 6
Others
Total 50 100 50 100

Marketing
Channels
P-LBA-M-E 25 50 20 40
P-S-LBA-M-E 25 50 30 60
Total 50 100 50 100

Variables Sharecrop-Managers

 Frequency Percentage
Age
21-30 10 20
31-40 15 30
41-50 15 30
Above 50 10 20
Total 50 100

Educational
Level
No formal 11 22
education
Primary 20 40
Secondary 15 30
Tertiary 2 4
Others 2 4
Total 50 100

Farm Size
1-5 43 86
6-10 2 4
Above 10 5 10
Total 50 100

Sources of
funds
Personal Savings 39 78
Bank loans 0 0
Informal Loans 6 12
Others
Total 50 100

Marketing
Channels
P-LBA-M-E 21 42
P-S-LBA-M-E 29 58
Total 50 100

Source: Field survey, 2002

Table 2: Descriptive statistics of costs and returns per hectare for
the three management systems.

 Costs

Statistic Owner- Lease- Sharecrop-
 managed managed managed

Mean 3,902.83 7,118.41 3,816.16
Median 2,080 1,840 1987.50
Standard 4,362.97 12,979.71 4,350.07
Deviation
Minimum 575 575 545.00
Maximum 17,150 51398.13 17,150

 Returns

Statistic Owner- Lease- Sharecrop-
 managed managed managed

 21,057.42 26,033.30 12,928.35
Mean 14,192.03 17,544.56 8,713.11
Median 28,954.95 35,798.20 17,776.72
Standard
Deviation 0 0 0
Minimum 139,355 172,296.19 85,556.14
Maximum

Source: Compiled from tables III, IV, and V

Table 3: Benefit-cost analysis for owner-managed farms.

Year yield (kg) Price Revenue Cost Discount
 (N/ton) (N/Ha) (N/Ha) Factor
 (10%)

1 0 1000 0 875 0.909
2 0 1453 0 625 0.826
3 0 2356 0 575 0.751
4 0 3259 0 718.33 0.685
5 273.11 4162 1136.67 861.67 0.621
6 273.11 5065 1383.29 1005 0.564
7 273.11 5968 1629.91 1148.33 0.513
8 546.22 6871 3753.05 1291.67 0.467
9 546.22 7775 4246.83 1435 0.424
10 819.3 8678 7110.09 1578.33 0.386
11 819.3 9581 7849.94 1721.67 0.35
12 819.3 10484 8589.78 1865 0.319
13 910.36 11387 10366.26 2008.33 0.29
14 1001.4 12290 12307.14 2151.67 0.263
15 1001.4 13193 13211.4 2295 0.239
16 1092.2 14096 15398.91 2438.33 0.218
17 1092.2 15000 16386.46 2581.67 0.198
18 1092.2 15000 16386.46 2725 0.18
19 1092.2 15000 16386.46 1600 0.164
20 1092.2 15000 16386.46 1850 0.149
21 1051.97 15000 15779.56 2850 0.135
22 1011.5 15000 15172.65 3450 0.122
23 1011.51 20000 20230.2 4850 0.112
24 933.7 30000 28011.05 4993 0.102
25 933.7 40000 37348.06 5145 0.092
26 855.89 50000 42794.66 10500 0.084
27 855.89 60000 51353.59 11797 0.076
28 855.89 60000 51353.59 12100 0.069
29 778.09 100000 77808.47 12900 0.063
30 700.28 199000 139355 17150 0.057

Year Discounted Discounted
 Cost (N) Revenue
 (N)

1 795.38 0
2 516.25 0
3 431.83 0
4 492.06 0
5 535.1 705.88
6 566.82 780.18
7 589.09 836.14
8 603.21 1752.67
9 608.44 1800.65
10 609.24 2744.49
11 602.59 2747.48
12 594.94 2740.14
13 582.42 3006.22
14 565.89 3236.78
15 548.505 3157.53
16 531.56 3356.96
17 511.17 3244.52
18 490.5 2949.56
19 262.4 2687.38
20 275.65 2441.58
21 384.75 2130.24
22 420.9 1851.06
23 543.2 2265.78
24 509.29 2857.13
25 473.34 3436.02
26 882 3594.75
27 896.57 3902.87
28 834.9 3543.4
29 812.7 4901.93
30 977.55 7943.23

NPV = N57,166.37
BCR = 4.27
Data analysis

Table 4: Benefit-cost analysis for lease managed farms.

Year yield (kg) Price Revenue Cost Discount
 (N/ton) (N/Ha) (N/Ha) Factor
 (10%)

 1 0 1000 0 955 0.909
 2 0 1453 0 625 0.826
 3 0 2356 0 575 0.751
 4 0 3259 0 685 0.685
 5 337.64 4162 1405.26 795 0.621
 6 337.64 5065 1710.15 905 0.564
 7 337.64 5968 2015.04 1015 0.513
 8 675.29 6871 4639.92 1125 0.467
 9 675.29 7775 5250.38 1235 0.424
 10 1012.89 8678 8789.86 1345 0.386
 11 1012.89 9581 9704.5 1455 0.35
 12 1012.89 10484 10619.14 1565 0.319
 13 1125.44 11387 12815.39 1675 0.29
 14 1237.98 12290 15214.77 1785 0.263
 15 1237.98 13193 16332.67 1895 0.239
 16 1350.48 14096 19036.37 2005 0.218
 17 1350.48 15000 20257.2 2115 0.198
 18 1350.48 15000 20257.2 2225 0.18
 19 1350.48 15000 20257.2 1600 0.164
 20 1350.48 15000 20257.2 2216.67 0.149
 21 1300.54 15000 19508.1 2833.33 0.135
 22 1250.43 15000 18756.45 3450 0.122
 23 1250.43 20000 25008.6 4850 0.112
 24 1154.28 30000 34628.4 4993 0.102
 25 1154.28 40000 46171.2 5145 0.092
 26 1058.1 50000 52905 14395.63 0.084
 27 1058.1 60000 63486 23646.25 0.076
 28 1058.1 60000 63486 32896.88 0.069
 29 961.91 100000 96191 42147.5 0.063
 30 865.81 199000 172296.2 51398.13 0.057

Year Discounted Discounted
 Cost (N) Revenue
 (N)
 1 868.1 0
 2 516.25 0
 3 431.83 0
 4 469.23 0
 5 493.7 872.67
 6 510.42 964.52
 7 520.7 1033.71
 8 525.4 2166.84
 9 523.64 2226.16
 10 519.17 3392.89
 11 509.25 3396.58
 12 499.24 3387.51
 13 485.75 3716.46
 14 469.46 4001.49
 15 452.91 3903.51
 16 437.09 4149.93
 17 418.77 4010.93
 18 400.5 3646.3
 19 262.4 3322.18
 20 330.28 3018.32
 21 382.5 2633.59
 22 420.9 2288.29
 23 543.2 2800.96
 24 509.29 3532.1
 25 473.34 4247.75
 26 1209.23 4444.02
 27 1797.12 4824.94
 28 2269.89 4380.53
 29 2655.29 6060.03
 30 2929.69 9820.88

NPV = N69,408.60
BCR = 4.04
Data analysis

Table 5: Benefit-cost analysis for sharecrop-managed farms.

Year yield (kg) Price Revenue Cost Discount
 (N/ton) (N/Ha) (N/Ha) Factor
 (10%)

1 0 1000 0 725 0.909
2 0 1453 0 600 0.826
3 0 2356 0 545 0.751
4 0 3259 0 692.5 0.685
5 167.67 4162 697.85 840 0.621
6 167.67 5065 849.26 987.5 0.564
7 167.67 5968 1000.67 1135 0.513
8 335.35 6871 2304.16 1282.5 0.467
9 335.35 7775 2607.31 1430 0.424
10 503.02 8678 4365.2 1553.89 0.386
11 503.02 9581 4819.42 1677.78 0.35
12 503.02 10484 5273.65 1801.67 0.319
13 558.91 11387 6364.3 1925.56 0.29
14 614.8 12290 7555.9 2049.44 0.263
15 614.8 13193 8111.06 2173.33 0.239
16 670.69 14096 9454.07 2297.22 0.218

17 670.69 15000 10060.37 2421.11 0.198
18 670.69 15000 10060.37 2545 0.18
19 670.69 15000 10060.37 1600 0.164
20 670.69 15000 10060.37 1850 0.149
21 645.85 15000 9687.76 2635.75 0.135
22 621.01 15000 9315.16 3421.5 0.122
23 621.01 20000 12420.21 4207.25 0.112
24 573.24 30000 17197.22 4993 0.102
25 573.24 40000 22929.62 5145 0.092
26 525.47 50000 26273.52 10500 0.084
27 525.47 60000 31528.23 11300 0.076
28 525.47 60000 31528.23 12100 0.069
29 477.7 100000 47770.04 12900 0.063
30 429.93 199000 85556.14 17150 0.057

Year Discounted Discounted
 Cost (N) Revenue
 (N)

1 659.03 0
2 495.6 0
3 409.3 0
4 474.36 0
5 521.64 433.37
6 5556.95 478.98
7 582.26 513.35
8 598.93 1076.04
9 606.32 1105.5
10 599.8 1684.97
11 587.22 1686.8
12 574.73 1682.29
13 558.41 1845.65
14 539 1987.2
15 519.43 1938.54
16 500.79 2060.97
17 479.38 1991.95
18 458.1 1810.87
19 262.4 1649.9
20 275.65 1499
21 355.83 1307.85
22 417.42 1136.45
23 471.2 1391.06
24 509.29 1754.12
25 473.34 2109.53
26 882 2206.98
27 858.8 2396.15
28 834.9 2175.45
29 812.7 3009.51
30 977.55 4876.7

NPV = N28,956.83
BCR = 2.72
Source: Data analysis
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Author:Nkang N.M.; Ajah, E.A.; Abang, S.O.; Edet E.O.
Publication:African Journal of Food, Agriculture, Nutrition and Development
Article Type:Report
Geographic Code:6NIGR
Date:Mar 1, 2009
Words:4993
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14 African Nations Agree on 30-Year Cocoa Sustainability Plan at Mars-Sponsored Consensus Conference in Ghana.
Ghana Cocoa Board: championing the cause of the cocoa farmer.

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