Innocent Bakam and Christopher Legg

System Dynamics for Landscape Modelling in Cameroon

Outline

• Modelling objectives

• Lessons learnt

• Difficulties

• Future developments

Modelling objectives

• improve understanding of the delicate balance between humans and their environment

• assist in formulating policies for future development

HOUSEHOLDLAND

PATCH

INFORMATION

LABOUR

YIELDS

MULTIPLE

PATCHES

MULTIPLE

HOUSEHOLDS

VILLAGE LAND

TENURESYSTEM 1

SYSTEM 2

GISGLOBAL

POPULATION

DECISIONS

INTERACTION

GLOBAL

DEVELOPMENT OF THE MODEL USING SIMILE PLATFORM

Where are we now?

• Fieldwork and other data collection complete

• Simile Models for Awae and Akok operational but not yet calibrated or validated

• Simile model for Nkometou almost complete

Main lessons learnt

1) Extreme complexity of cropping systems in African humid forests, evolved to ensure food security and minimise labour demands

2) Labour availability is critical to all farming activities and household decisions

3) Population pressures force gradual abandonment of traditional farming systems at densities greater than about 20 persons per square kilometre

4) Understanding land use and tenure patterns requires careful planning and development of sampling techniques

A dominant dynamic is the conversion of forest and fallow to cultivation in a twice-yearly cycle of

slash-and-burn

Logged and Secondary Forest

Melon/Plantain System

Cocoa System

Mixed Food System

Fallow

6-9 years

Wetland Maize

Oil Palm

Land use dynamics in the humid forest zone of

Cameroon

Main lessons learnt (2)

1) Agricultural intensification, with shorter fallows, smaller fields, and increasing emphasis on mono-culture cash-crops can support population densities in excess of 100 persons per square kilometre, but only at the cost of soil degradation or high levels of inputs

2) Forest bio-diversity is rarely maintained at population densities above 20/km2

3) In areas where forest is still abundant, most clearing of forest is for nutritionally unproductive essep (forest melon) fields, not for mixed-food fields

0

10

20

30

40

50

60

70

80

90

Akok Awae Nkometou

village

Area/HH

pop/km2

Village Area HouseholdsArea/HH HH/km2 pop/km2Akok 9480 124 76.5 1.3 7.8Awae 820 49 16.7 6.0 35.9Nkometou 1463 208 7.0 14.2 85.3

Available land per household decreases from 75 hectares in Awae to 7 hectares in Nkometou

Population density increases from 8 persons per square kilometre to

85

 

Average size of mixed-food fields declines from 0.24 hectares in Akok to 0.10 hectares in Nkometou

Average size of cocoa fields declines from 1.32 hectares to 0.66 hectares

Field Sizes

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Akok Awae Nkometou

Hec

tare

s

Asane

Cocoa

Essep

Mixed food 1

Mixed food 2

Maize

Tomato

Potato

Other Veg

Asane and essep are no longer cultivated in Nkometou because of land shortage and lack of forest.

A range of market-orientated monocultures have become important instead

0%

20%

40%

60%

80%

100%

Nkometou Awae Akok

villages

per

cen

tag

e cu

ltiv

atio

n

forest melons

monocultures

mixed food

cocoa

  Nkometou Awae Akok

cocoa 26.2 41.2 41.8

mixed food 38.2 41.6 48.7

monocultures 35.6 8.4 2.2

forest melons 0.0 8.8 7.3

Proportion of cultivated land under mixed food crops

declines from 49% in Akok to 38% in Nkometou

Proportion under monocultures such as maize and tomatoes increases from 2% in Akok to

36% in Nkometou

Forest melon/plantain fields decline from 7-9% in Akok

and Awae to zero in Nkometou

Relative importance of cocoa declines as more fallow is

cultivated as monocultures

Difficulties• Lack of data and agronomic expertise in

modelling team to calibrate and validate the model

• Lack of time to achieve the numerous challenges raised by the modelling activity (lead scientist only part-time in modelling programme for past two years)

• Problems with the Simile platform which is still under development

Future developments (dependent on funding)

• Calibration, to ensure that outputs are in reasonable ranges

• Validation of all sub models within a multi-disciplinary team (agronomist, pedologist, sociologist, economist, modeler), to ensure that the model is scientifically valid

• Development of more friendly user interfaces to ease the adoption of the model by end-users

The Model has become extremely complex

Future developments (2)

• Validation in the field with stakeholders (farmers, NGO, policy makers) to ensure that the model reflects the different points of view

• Testing of different scenarios and development of appropriate development strategies

• Extrapolation of outcomes to Benchmark and eco-zone

• Dissemination of model results through publications, role games and seminars