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• W1552391680 abstract "[1] We present a piecewise linear decision tree model for predicting percent of soil organic C (SOC) in the agricultural zones of Australia generated using a machine learning approach. The inputs for the model are a national database of soil data, national digital surfaces of climate, elevation, and terrain variables, Landsat multispectral scanner data, lithology, land use, and soil maps. The model and resulting map are evaluated, and insights into biogeological surficial processes are discussed. The decision tree splits the overall data set into more homogenous subsets, thus in this case, it identifies areas where SOC responds closely to climatic and other environmental variables. The spatial pattern of SOC corresponds well to maps of estimated primary productivity and bioclimatic zones. Topsoil organic C levels are highest in the high rainfall, temperate regions of Tasmania, Victoria, and Western Australia, along the coast of New South Wales and in the wet tropics of Queensland; and lowest in arid and semiarid inland regions. While this pattern broadly follows continental vegetation, soil moisture, and temperature patterns, it is governed by a spatially variable hierarchy of different climatic and other variables across bioregions of Australia. At the continental scale, soil moisture level, rather than temperature, seems most important in controlling SOC." @default.
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• W1552391680 date "2009-12-01" @default.
• W1552391680 modified "2023-10-18" @default.
• W1552391680 title "Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia" @default.
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• W1552391680 doi "https://doi.org/10.1029/2009gb003506" @default.
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