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  • Assessment of soil group, s...
    Nanko, K.; Hashimoto, S.; Miura, S.; Ishizuka, S.; Sakai, Y.; Levia, D. F.; Ugawa, S.; Nishizono, T.; Kitahara, F.; Osone, Y.; Kaneko, S.

    European journal of soil science, 07/2017, Volume: 68, Issue: 4
    Journal Article

    The aim of this study was to assess the factors that account for the geographical variation in soil organic carbon stocks at the 0–30‐cm depth ( SOC 30 ) of forests in J apan. Boosted regression tree analysis was applied to 2157 points throughout J apan and to four regional geographical subdivisions with 16 environmental variables. The rank of predictor variables was different for J apan as a whole and among the regions. For J apan as a whole, soil group, air temperature, slope inclination, altitude and organic carbon stocks of litter were the most important factors that affected SOC 30 stocks. Overall, SOC 30 stocks decreased with air temperature, which was attributed to the decomposability of organic carbon. In addition, SOC 30 stocks decreased with slope inclination because of instability of the topsoil on slopes, which, in turn, is related to the increase in rock fragment content and decrease in soil bulk density. The distribution of volcanic soil resulted in larger SOC 30 stocks than was expected from climatic conditions. Precipitation was not important because of conflicting effects between the increase in soil organic carbon content with increasing net primary production and the decrease in mineral soil mass by the loss in topsoil. The regional analyses provide insight into the factors that cause variation in SOC 30 stocks, which were obscured by the macroscale analysis of Japan as a whole, thereby illustrating the power of regional geographical analyses. Our results provide an improved basis for soil, forestry and biogeochemical models that require accurate estimates of SOC 30 stocks. Highlights We assessed factors that account for geographic variation in soil organic carbon ( SOC ) stocks. Effect and dependence of factors were estimated by a machine learning approach. SOC stocks were affected by soil type, climate, site‐specific location and organic matter input. Volcanic soil distribution, climate, slope steepness and historical overuse of forest affected SOC stocks.