Modeling Agricultural Change through Logistic Regression and Cellular Automata: A Case Study on Shifting Cultivation

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.