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Pedology

Prediction of Soil Organic Carbon across Different Land-use Patterns

A Neural Network Approach

^a Dep. of Botany, The Open Univ. of Sri Lanka, Nawala, Nugegoda, Sri Lanka
^b Institute of Fundamental Studies, Hantana Road, Kandy, Sri Lanka
^c Vice Chancellor's Office, The Open Univ. of Sri Lanka, Nawala, Nugegoda, Sri Lanka

^* Corresponding author (gaminis{at}ifs.ac.lk)

Mathematical modeling has widely been used to predict soil organic carbon (SOC). However, there are characteristics of the models such as over simplification, ignorance of complex nonlinear interactions etc., which limit their use in accurately assessing the distribution of the C across the landscapes. Artificial neural network (ANN) modeling approach that provides a tool to solve complex problems related to larger data sets was therefore used here to predict SOC contents across different land use patterns in a study conducted in Sri Lanka. Selection of variables was made using a priori knowledge of the relationships between the variables. Thus, soils of the sites were sampled and analyzed for organic C by internal heat of dilution (Ci) and external heat of dilution (Ce), and the results were presented as grams per kilogram (g kg^–1). In addition, some landscape attributes and environmental parameters of the sites were also collected. The predictive performance of ANN was compared with multi-linear regression (MLR) models. The best ANN model predicted the measured Ci content with R² of 0.92. However, comparison of the two types of models indicated less bias and high accuracy of the ANN compared with MLR in predicting Ci, but the reverse for Ce. In order to better predict Ce, it is recommended to use other architectures of neural networks and training algorithms for improving predictive accuracy. The predictive capability of the ANN developed with easily available climatic and terrain data are of importance in predicting SOC with minimum cost, labor, and time.

Abbreviations: AIC, Akaike's information criterion • ANN, artificial neural network • Ce, soil organic carbon determined by application of external heat • CEC, cation exchange capacity • Ci, SOC determined by internal heat of dilution • LSD, least significant difference • MLR, multi-linear regression • MR, mean residuals • RMSE, root mean sums of squared error • RMSR, root mean square of residuals • SOC, soil organic carbon • TA, transformed aspect

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