Abstract
In this study we map the distribution and dynamics of commodity-level agricultural land use in the Murray–Darling Basin, Australia for two snapshot years 1996/1997 and 2000/2001. In the process, we integrate a diverse set of information including time-series remote sensing, agricultural statistics, field data from control sites and geographic information system data. A Bayesian Monte Carlo Markov chain technique is combined with areal interpolation to map the spatial distribution and dynamics of 48 agricultural commodities at 1.1 km resolution. We use the maps to assess land use change in the Murray–Darling Basin including a 22% increase in the area of irrigated agriculture and discuss the impacts for the management of land and water resources. The high-resolution outputs are suitable for other applications including spatially explicit economic modelling, assessments of natural resource use, and for informing agricultural, water and natural resource management planning and policy.
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