Physical Landscape Correlates of the Expansion of Mechanized Agriculture in Mato Grosso, Brazil

Abstract

Mechanized agriculture is rapidly expanding in the state of Mato Grosso, Brazil. In the past five years, land area planted with soybeans, the state?s principal crop, has increased at an average rate of 19.4% yr?1. Drivers of this large-scale land-use conversion are principally economic and sociopolitical, but physical properties of the landscape make some areas more attractive than others for expansion of mechanized agriculture. The goal of this study is to evaluate several physical characteristics of land in Mato Grosso and to quantify their respective weights in determining the likelihood of land-use conversion to crop production. A 2003 land-cover classification at 250-m resolution was compared to maps of five physical landscape characteristics (surface slope, soil type, total November precipitation, distance from paved roads, and previous land-cover type based on a 2001 classification). A land-cover transition matrix was generated to inform analysis of the role of previous land-cover type, and statewide distributions of the other four landscape characteristics were examined across agricultural and nonagricultural land. Finally, logistic regressions were performed to quantify the respective correlations of these various characteristics with the probability of conversion to mechanized agriculture. Areas of new cropland in 2003 (converted since the 2001 classification) were nearly 3 times as likely to have been converted from pasture/cerrado as from all other land-cover types combined, but in terms of class original extent, bare soil was by far the most likely class to be converted to cropland, with 56% of its 2001 land area being converted by 2003. The physical landscape parameter found most highly correlated with conversion to mechanized agriculture between 2001 and 2003 was that of the previous land-cover type, followed by topographic slope and distance from paved roads. Soil type and total November precipitation were poorly correlated with mechanized agriculture. Findings from this study suggest that holistic, spatially explicit models of likelihood of conversion to mechanized agriculture should consider land cover, slope, and proximity to main roads in addition to political and economic parameters to generate realistic scenarios for sustainable land-use planning.