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African Journal of Food, Agriculture, Nutrition and Development
Rural Outreach Program
ISSN: 1684-5358 EISSN: 1684-5374
Vol. 9, Num. 2, 2009, pp. 713-727
INVESTMENT IN COCOA PRODUCTION IN NIGERIA: A COST AND RETURN ANALYSIS OF THREE COCOA PRODUCTION MANAGEMENT SYSTEMS IN THE CROS

African Journal of Food Agriculture Nutrition and Development, Vol. 9, No. 2, March, 2009  

Investment in Cocoa Production In Nigeria: A Cost And Return Analysis Of Three Cocoa production Management Systems In The Cross River State Cocoa Belt

Nkang N M *1 , EA Ajah2 , SO Abang3   and EO Edet4

*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

Code Number: nd09019

ABSTRACT

The study examined costs and returns in cocoa production in Cross River State by comparing three identified management systems of cocoa production in the area. A two-stage sampling procedure was used to select a hundred and fifty cocoa farmers for the study. Data used in the study were collected using structured questionnaires which were administered by the Agricultural Development Programme (ADP) extension agents using the participatory approach while the data were analysed using descriptive statistics such as mean, median, standard deviation, etc. and an investment decision model comprising the net present value (NPV) and benefit-cost ratio (BCR) analysis. Results show that the respondents were predominantly small scale farmers with farm sizes ranging from one to five hectares.  The age distribution of the farmers showed that 61.3% of them were among the active farming population falling within the age range of 21 to 40 years, and 16.67% of the respondents had no formal education.  More than 50% of the total respondents sourced funds from their personal savings in all the management systems considered. Importantly, the study found that cocoa production is a profitable business irrespective of management system, since all of the management systems had positive net present values (NPV) at 10% discount rate.  The NPV for lease-managed farms is highest.  The benefit-cost ratio (BCR) at 10% discount rate was greater than one for all 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 and sharecropped farms in that order.  Lease-managed farms were more viable compared with other management systems in terms of their high NPVs. 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 by providing expanded access to cheap and flexible credit and land, which have presented as limiting factors in cocoa production based on the descriptive statistical analysis in the study. 

Key words: Cocoa, benefit, cost, investment, management

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 INCit  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:

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

Where

REVi,t = the expected revenue per hectare from i-year-old trees in year t;

TCi,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:

Where:            

DREVi,t = discounted revenue (benefits) per hectare from i-year-old trees in year t;

DTCi,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. 

Table 1:          Socioeconomic characteristics of cocoa farmers in Cross River State.

Variables

Owner-Managers

Leased- Managers

Sharecrop-Managers

 

Frequency

Percentage

Frequency

Percentage

Frequency

Percentage

Age

21-30

7

14

10

20

10

20

31-40

20

40

20

40

15

30

41-50

15

30

10

20

15

30

Above 50

8

16

10

20

10

20

Total

50

100

50

100

50

100

 

 

 

 

 

 

Educational Level

 

 

 

 

 

 

No formal education

10

20

4

8

11

22

Primary

13

26

10

20

20

40

Secondary

14

28

26

52

15

30

Tertiary

6

12

7

14

2

4

Others

7

14

3

6

2

4

Total

50

100

50

100

50

100

 

 

 

 

 

 

Farm Size

 

 

 

 

 

 

1-5

32

64

45

90

43

86

6-10

14

28

5

10

2

4

Above 10

4

8

0

0

5

10

Total

50

100

50

100

50

100

 

 

 

 

 

 

Sources of funds

 

 

 

 

 

 

Personal Savings

38

76

37

74

39

78

Bank loans

3

6

6

12

0

0

Informal Loans

2

4

3

6

6

12

Others

 

 

 

 

 

 

Total

50

100

50

100

50

100

 

 

 

 

 

 

Marketing Channels

 

 

 

 

 

 

P-LBA-M-E

25

50

20

40

21

42

P-S-LBA-M-E

25

50

30

60

29

58

Total

50

100

50

100

50

100

Source: Field survey, 2002

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. 

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

Costs

Returns

Statistic

Owner-managed

Lease-managed

Sharecrop-managed

Owner-managed

Lease-managed

Sharecrop-managed

Mean

3,902.83

7,118.41

3,816.16

21,057.42

26,033.30

12,928.35

Median

2,080

1,840

1,987.5

14,192.03

17,544.56

8,713.11

Standard Deviation

4,362.97

12,979.71

4,350.07

28,954.95

35,798.20

17,776.72

Minimum

575

575

545

0

0

0

Maximum

17,150

51,398.13

17,150

139,355

172,296.19

85,556.14

Source: Compiled from tables III, IV, and V

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.

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

Year

yield (kg)

Price (N/ton)

Revenue (N/Ha)

Cost (N/Ha)

Discount

Factor (10%)

Discounted

Cost (N)

Discounted

Revenue (N)

1

0

1000

0

875

0.909

795.38

0

2

0

1453

0

625

0.826

516.25

0

3

0

2356

0

575

0.751

431.83

0

4

0

3259

0

718.33

0.685

492.06

0

5

273.11

4162

1136.67

861.67

0.621

535.1

705.88

6

273.11

5065

1383.29

1005

0.564

566.82

780.18

7

273.11

5968

1629.91

1148.33

0.513

589.09

836.14

8

546.22

6871

3753.05

1291.67

0.467

603.21

1752.67

9

546.22

7775

4246.83

1435

0.424

608.44

1800.65

10

819.3

8678

7110.09

1578.33

0.386

609.24

2744.49

11

819.3

9581

7849.94

1721.67

0.35

602.59

2747.48

12

819.3

10484

8589.78

1865

0.319

594.94

2740.14

13

910.36

11387

10366.26

2008.33

0.29

582.42

3006.22

14

1001.4

12290

12307.14

2151.67

0.263

565.89

3236.78

15

1001.4

13193

13211.4

2295

0.239

548.505

3157.53

16

1092.2

14096

15398.91

2438.33

0.218

531.56

3356.96

17

1092.2

15000

16386.46

2581.67

0.198

511.17

3244.52

18

1092.2

15000

16386.46

2725

0.18

490.5

2949.56

19

1092.2

15000

16386.46

1600

0.164

262.4

2687.38

20

1092.2

15000

16386.46

1850

0.149

275.65

2441.58

21

1051.97

15000

15779.56

2850

0.135

384.75

2130.24

22

1011.5

15000

15172.65

3450

0.122

420.9

1851.06

23

1011.51

20000

20230.2

4850

0.112

543.2

2265.78

24

933.7

30000

28011.05

4993

0.102

509.29

2857.13

25

933.7

40000

37348.06

5145

0.092

473.34

3436.02

26

855.89

50000

42794.66

10500

0.084

882

3594.75

27

855.89

60000

51353.59

11797

0.076

896.57

3902.87

28

855.89

60000

51353.59

12100

0.069

834.9

3543.4

29

778.09

100000

77808.47

12900

0.063

812.7

4901.93

30

700.28

199000

139355

17150

0.057

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 (N/ton)

Revenue (N/Ha)

Cost (N/Ha)

Discount Factor (10%)

Discounted

Cost (N)

Discounted

Revenue (N)

1

0

1000

0

955

0.909

868.1

0

2

0

1453

0

625

0.826

516.25

0

3

0

2356

0

575

0.751

431.83

0

4

0

3259

0

685

0.685

469.23

0

5

337.64

4162

1405.26

795

0.621

493.7

872.67

6

337.64

5065

1710.15

905

0.564

510.42

964.52

7

337.64

5968

2015.04

1015

0.513

520.7

1033.71

8

675.29

6871

4639.92

1125

0.467

525.4

2166.84

9

675.29

7775

5250.38

1235

0.424

523.64

2226.16

10

1012.89

8678

8789.86

1345

0.386

519.17

3392.89

11

1012.89

9581

9704.5

1455

0.35

509.25

3396.58

12

1012.89

10484

10619.14

1565

0.319

499.24

3387.51

13

1125.44

11387

12815.39

1675

0.29

485.75

3716.46

14

1237.98

12290

15214.77

1785

0.263

469.46

4001.49

15

1237.98

13193

16332.67

1895

0.239

452.91

3903.51

16

1350.48

14096

19036.37

2005

0.218

437.09

4149.93

17

1350.48

15000

20257.2

2115

0.198

418.77

4010.93

18

1350.48

15000

20257.2

2225

0.18

400.5

3646.3

19

1350.48

15000

20257.2

1600

0.164

262.4

3322.18

20

1350.48

15000

20257.2

2216.67

0.149

330.28

3018.32

21

1300.54

15000

19508.1

2833.33

0.135

382.5

2633.59

22

1250.43

15000

18756.45

3450

0.122

420.9

2288.29

23

1250.43

20000

25008.6

4850

0.112

543.2

2800.96

24

1154.28

30000

34628.4

4993

0.102

509.29

3532.1

25

1154.28

40000

46171.2

5145

0.092

473.34

4247.75

26

1058.1

50000

52905

14395.63

0.084

1209.23

4444.02

27

1058.1

60000

63486

23646.25

0.076

1797.12

4824.94

28

1058.1

60000

63486

32896.88

0.069

2269.89

4380.53

29

961.91

100000

96191

42147.5

0.063

2655.29

6060.03

30

865.81

199000

172296.2

51398.13

0.057

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 (N/ton)

Revenue (N/Ha)

Cost (N/Ha)

Discount

Factor (10%)

Discounted

Cost (N)

Discounted

Revenue (N)

1

0

1000

0

725

0.909

659.03

0

2

0

1453

0

600

0.826

495.6

0

3

0

2356

0

545

0.751

409.3

0

4

0

3259

0

692.5

0.685

474.36

0

5

167.67

4162

697.85

840

0.621

521.64

433.37

6

167.67

5065

849.26

987.5

0.564

5556.95

478.98

7

167.67

5968

1000.67

1135

0.513

582.26

513.35

8

335.35

6871

2304.16

1282.5

0.467

598.93

1076.04

9

335.35

7775

2607.31

1430

0.424

606.32

1105.5

10

503.02

8678

4365.2

1553.89

0.386

599.8

1684.97

11

503.02

9581

4819.42

1677.78

0.35

587.22

1686.8

12

503.02

10484

5273.65

1801.67

0.319

574.73

1682.29

13

558.91

11387

6364.3

1925.56

0.29

558.41

1845.65

14

614.8

12290

7555.9

2049.44

0.263

539

1987.2

15

614.8

13193

8111.06

2173.33

0.239

519.43

1938.54

16

670.69

14096

9454.07

2297.22

0.218

500.79

2060.97

17

670.69

15000

10060.37

2421.11

0.198

479.38

1991.95

18

670.69

15000

10060.37

2545

0.18

458.1

1810.87

19

670.69

15000

10060.37

1600

0.164

262.4

1649.9

20

670.69

15000

10060.37

1850

0.149

275.65

1499

21

645.85

15000

9687.76

2635.75

0.135

355.83

1307.85

22

621.01

15000

9315.16

3421.5

0.122

417.42

1136.45

23

621.01

20000

12420.21

4207.25

0.112

471.2

1391.06

24

573.24

30000

17197.22

4993

0.102

509.29

1754.12

25

573.24

40000

22929.62

5145

0.092

473.34

2109.53

26

525.47

50000

26273.52

10500

0.084

882

2206.98

27

525.47

60000

31528.23

11300

0.076

858.8

2396.15

28

525.47

60000

31528.23

12100

0.069

834.9

2175.45

29

477.7

100000

47770.04

12900

0.063

812.7

3009.51

30

429.93

199000

85556.14

17150

0.057

977.55

4876.7

NPV =

N28,956.83

BCR =

2.72

Source: Data analysis

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 economictheory. 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 analysisresults 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.

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