Soil organic carbon is one of the most important indicators of soil health and factors controlling soil productivity (Sanchez et al., 1997; Lal and Kimble, 2000). It stands a great chance of becoming a marketable commodity because of its influence on both soil productivity and atmospheric chemistry. The dynamics of soil organic carbon are controlled by many factors affecting the balance between its buildup and breakdown processes. These processes are influenced by climate, soil type, land use, and management regime (Tenywa et al., 1999). Although the impact of each of these factors is known, soil organic carbon dynamics driven by the above factors combined do not display an obvious trend. Therefore, a simple and robust tool for assessing soil organic carbon dynamics in various environments and guiding management and policy decisions across land uses in the course of time is needed.
Most of the conventional methods for quantification of soil organic carbon have limitations in capturing the dynamics of its sequestration processes resulting from spatial and temporal changes in one or more of the factor controls. Modeling techniques have been deemed useful in organizing and synthesizing multiple data sets and providing valuable insights for developing a scientifically defensible soil organic carbon sequestration accounting system. However, the available models can only be used for fine scale (Van Veen et al., 1984; Parton et al., 1994). The objective of this study was to integrate the knowledge of climate, soil, land use, and management conditions on variability of soil organic carbon in different agroclimatic zones of Uganda.
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