TITLE:
Modeling Agricultural Change through Logistic Regression and Cellular Automata: A Case Study on Shifting Cultivation
AUTHORS:
Santiago Lopez
KEYWORDS:
Logistic, Cellular Automata, GIS, Shifting Cultivation, Land Cover, Amazon
JOURNAL NAME:
Journal of Geographic Information System,
Vol.6 No.3,
June
18,
2014
ABSTRACT: Agricultural expansion is one
of the prime driving forces of global land cover change. Despite the increasing
attention to the factors that cause it, the patterns and processes associated
with indigenous cultivation systems are not well understood. This study
analyzes agricultural change associated with subsistence-based indigenous
production systems in the lower Pastaza River Basin in the Ecuadorian Amazon
through a spatially explicit dynamic model. The model integrates multiple
logistic regression and cellular automata to simulate agricultural expansion at
a resolution consistent with small scale agriculture and deal with inherently
spatial processes. Data on land use and cultivation practices were collected
through remote sensing and field visits, and processed within a geographic
information system framework. Results show that the probability of an area of
becoming agriculture increases with population pressure, in the vicinity of
existing cultivation plots, and proximity to the center of human settlements.
The positive association between proximity to cultivation areas and the
probability of the presence of agriculture clearly shows the spillover effect
and spatial inertia carried by shifting cultivation practices. The model
depicts an ideal shifting cultivation system, with a complete
cropping-fallow-cropping cycle that shows how agricultural areas expand and
contract across space and over time. The model produced relatively accurate
spatial outputs, as shown by the results of a spatial comparison between the
simulated landscapes and the actual one. The study helped understand local
landscape dynamics associated with shifting cultivation systems and their
implications for land management.