Are the Cocoa Agroforests of Southern Cameroon a High Carbon Pathway to

Development?

By James Gockowski

Sustainable Tree Crops Program

World Congress of Agroforestry 2009, Nairobi, Kenya

August 25, 2009

Introduction to the problem

April 7, 2006 Conservation and the Agricultural Frontier “Values of biodiversity in southern Cameroon”

Yale FES ISTF Conference

Int’l Inst. of Trop. Ag. Sustainable Tree Crops

Program

Annual Closed Deforestation canopy forest Cocoa(%) (million ha) (million ha)

Cote D’Ivoire 3.5% 2.4 2.5-3.0 Nigeria 2.5% 2.6 0.5-1.0Ghana 1.7% 1.8 1.5-2.0Cameroon 0.89% 19.5 0.2-0.4Source: World Bank, World Resources Institute, FAOSTAT

Rural Poverty(%)

Cote D’Ivoire no data

Nigeria 37% Ghana 50% Cameroon 50%

Cultivation-Forest Mosaic Dominated by Cocoa Cropping System

Original Climax Ecosystems and Cultivation-Forest Mosaic in WCA

Main Research Questions

To what extent do extant cocoa agroforests offer a viable pathway out of chronic poverty?

What role for research?

April 7, 2006 Conservation and the Agricultural Frontier “Values of biodiversity in southern Cameroon”

Yale FES ISTF Conference

Int’l Inst. of Trop. Ag. Sustainable Tree Crops

Program

Equatorial Guinea Gabon

Rural inhabitants per km2

71-90

51-70

31-50

21-30

11-20

5-10

<5

urban Yaounde

Cameroon

Lékié

Mefou

Mvila

Gradient of agricultural intensification/market access in FM benchmark

Importance of CAFs in the provision of environmental services

Depends on their extent in the landscape: Cocoa lands accounted for 684 km, 439 and

624 sq. km (equivalent to 23, 9 and 4 percent of total land area) in the Lékié, Mfou and Mvila divisions

Santoir (1995) has shown that in some specific localities of the intensified Lékié CAF area cover>30% of total area.

cocoamixed food cropmixed food last seasonfallow

Land use legend

300 m

Findings: Non-Timber Product Inventory

Type of use values: 93 species with food value, 186 species with medicinal value and 103 species used for other purposes (food wrapping, poles for

construction, caterpillar trees, etc.) Plant type:

165 tree species 13 woody shrubs, 30 vines and 46 herbaceous plants

Most represented botanical families: Euphorbiaceae--16 species Sterculiaceae--12 species and Apocynaceae, Caesalpiniaceae and Moraceae--11 species each

254 plant species from 78 different families w/ some consumptive value.

Most Frequent Food Species

74%Ricinodendron heudelotii (Baill.) Heckel

74%Elaeis guineensis Jacq.

76%Myrianthus arboreus P. Beauv.

78%Terminalia superba Engl. & Diels

83%Ficus exasperata Vahl

87%Pycnanthus angolensis (Welw.) Warb.

93%Costus afer Ker Gawl.

93%Mangifera indica L.

96%Persea americana Mill.

100%Dacryodes edulis (G. Don) H.J. Lam

Food Uses Presence(% of CAFs)

D. edulis, African plum

Oil palm Elaeis guineensis

Biodiversity and Fruit Tree Density

3

3.5

4

4.5

5

5.5

6

0 20 40 60 80 100 120 140

FRUIT TREE DENSITY PER HA

SH

AN

NO

N I

ND

EX

Fruit Tree Density and Market Access

0

2

4

6

8

10

12

14

16

Citrus Mangos Avocados African plum

SPECIES

TR

EE

S P

ER

HA

Poor Access

Good Access

Commercialization

Market participation for non-timber products varied across sites. 100% in the Lékié 77% in the Mefou and 53% in the Mvila sites

Timber revenues reported by 67% of households in Lékié sites versus 33% in the Mefou and 0% in the Mvila.

Women were most frequently responsible for the sale of non-cocoa products with the exceptions of palm wine and timber

Non-cocoa revenues were most often controlled by women (57% of households)

Findings: Gross Margin per ha

741

425 397

201

217

51

0

100

200

300

400

500

600

700

800

900

1000

Lékié Mefou Mvila

INTENSIFICATION GRADIENT

RE

VE

NU

ES

(U

SD

$ P

ER

HA

)

Non Cocoa

Cocoa

Per capita revenue distribution Per capita CAF revenues were in excess of the

poverty line for 46%, 25% and 15% of individuals living on cocoa farms in the Lékié, Mefou and Mvila sites, respectively.

The distribution is positively skewed with the highest quintile of the per capita revenue distribution accounting for 70% of total income (versus 2.3% for the lowest quintile).

Overall, CAF revenues increased and poverty decreased as agricultural intensification and market access increased along the Mvila-Mefou-Lékié gradient.

0

20

40

60

80

100

120

140

Per Capita Income (USD $/day)

No

. o

f In

div

idu

als

Poverty line

Median = $0.50Mean = $0.95Std dev = $1.09

(a)

(b)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

< $0.25 < $1.00 < $1.75 < $2.50 < $3.25 < $4.00 < $4.75 < $5.50

Per Capita Income (USD $/day)

Cu

mu

lati

ve F

req

uen

cy

Poverty line$1 per day

MvilaMefouLékié

PER CAPITA INCOME (USD $/DAY)

PER CAPITA INCOME (USD $/DAY)

NO

. OF

IN

DIV

IDU

AL

SC

UM

UL

AT

IVE

FR

EQ

UE

NC

Y

A Comparison of Households from the Upper and Lower Quintiles of the Revenue Distribution

Households Lower 20% Upper 20% Cocoa yield (kg/ha) 254 688 Fungicide (sachets per ha) 23 42 Non-cocoa revenues (USD$ per ha) 53 266 Size of cocoa farm (ha) 1.63 3.95 Mean no. of non-cocoa products sold 2 4 Travel time to farms (min.) 29 17 Non-cultivated area in forest (ha) 28 18 Fallow period 8 6 Family size 8 4 Dependency ratio 2.2 1.3

Conclusions

Poverty reduction of systems depends on cocoa intensification and the commercialization of non-cocoa products.

Most important commercial associations were cocoa, oil palm, plum (Dacryodes), mango, and avocado

Maintains a substantial portion of forest services

Some decline in biodiversity occurred over the gradient of agricultural intensification.

Recommendations for poverty alleviation via CAF development

Intensify the cocoa component of these systems by increasing farmer access to fungicides through provision of credit facilities.

Improve the efficiency and extent of non-cocoa commodity marketing by targeting women in: The organization of collective marketing to achieve

economies of scale and The inclusion of CAF fruit prices in market information

systems; Incentives to expand the area cultivated by the poor.

(More study of tree investment behavior may be needed to guide these efforts).

Research has completely neglected these systems (what are optimal densities