Mathematical modeling has widely been used to predict soil organic ingful in creating a real picture of spatial distribution carbon (SOC). However, there are characteristics of the models such as over simplification, ignorance of complex nonlinear interactions of SOC. Attempts have been made to estimate global etc., which limit their use in accurately assessing the distribution of the SOC using the pedon database and extrapolating them C across the landscapes. Artificial neural network (ANN) modeling to soil units of the world soil map (Bohn, 1976, 1982; approach that provides a tool to solve complex problems related to Batjes, 1996; Buringh, 1984; Kimble et al., 1990). The larger data sets was therefore used here to predict SOC contents pedon database of the USDA Soil Conservation Service across different land use patterns in a study conducted in Sri Lanka. and related organizations has been used to estimate the Selection of variables was made using a priori knowledge of the regional distribution of organic C in the USA (Kern, relationships between the variables. Thus, soils of the sites were sam-1994). However, previous studies indicated that there pled and analyzed for organic C by internal heat of dilution (Ci) and are uncertainties associated with such SOC estimates external heat of dilution (Ce), and the results were presented as grams and often related to variations in soil map scales and per kilogram (g kg Ϫ1). In addition, some landscape attributes and environmental parameters of the sites were also collected. The pre-series. As a whole the uncertainties associated with meadictive performance of ANN was compared with multi-linear regres-suring and detecting changes in soil C pools remain sion (MLR) models. The best ANN model predicted the measured high, both at individual sites and extrapolating site-level Ci content with R 2 of 0.92. However, comparison of the two types of data to regional, national, or global scales (Vance, 2003). models indicated less bias and high accuracy of the ANN compared Accurate and precise approaches yet to be available for with MLR in predicting Ci, but the reverse for Ce. In order to better assessing the effect of management practices and land predict Ce, it is recommended to use other architectures of neural use change on the soil C for the purpose of incorporation networks and training algorithms for improving predictive accuracy. of this important pool into future C accounting systems. The predictive capability of the ANN developed with easily available The Kyoto Protocol, for instance, limits reporting of C climatic and terrain data are of importance in predicting SOC with sequestration activities to "measurable and verifiable" minimum cost, labor, and time. pools (Vance, 2003). Mathematical modeling has been used to predict soil C evolution (Jenkinson and Rayner, 1977; Parton et al.
