Modeling and characterizing agricultural expansion:
a case study in Senegal, West Africa.
Eric Channing Wood - 2002
[UMI Proquest Full
The overall goal of this study was to better understand
the drivers of the expansion of agriculture in rural Senegal, and was attained
through research focusing on two major objectives. (1) To determine
an accurate approach for modeling the expansion of agriculture in southern
Senegal. This objective required the comparison of multiple runs of the artificial
neural network based Land Transformation Model, using a variety of parameterizations.
Model modifications were made that maximized temporal generalization through
the incorporation of land use/land cover change trajectories. (2) To
explore the factors that are driving the expansion of agriculture in southern
Senegal. Addressing this objective involved both modeling and detailed field
investigations. A theoretical framework that emphasizes “extensification
vs. intensification” of agriculture along the lines of the neo-Boserup-Malthus
contradiction was used for assessing findings resulting from these investigations.
Results of the study indicate that the department of Velingara is meeting
its agricultural production needs through extensification, rather than intensification,
of agriculture. All evidence, both from the field and through analysis of
remotely sensed data, suggests that shifting agriculture continues to be
the primary agricultural system in place in the department. Velingara fails
to meet several of the criteria necessary to move from extensification, or
labor-led intensification of agriculture, to a more sustainable capital-led
move towards intensification. Another key result was the determination that
an artificial neural network (ANN) based model was able to accurately model
the expansion of agriculture in Velingara. KHAT statistics indicate that
this model predicts transitions to agriculture at a level significantly greater
than random chance. It was also shown that trajectory of change concepts
can be incorporated into ANN-based models and provide significant increases
in predictive accuracy.