An application of genetic algorithms to the robust estimation of soil organic and mineral fraction densities

The development and use of a program that combines genetic algorithms and the least median of squares (LMS) method is reported. The combination of these two methods should provide a very robust approach for optimising non-linear functions. The program was specifically developed to estimate soil bulk density using a mass fraction model and was tested against a simulated dataset before being applied to the prediction of bulk density from a dataset created by combining measurements from a number of studies. The prediction of bulk density has a very important role in the calculation of soil elemental pools, and the predictive ability provided by the models described can be used to improve estimates of the key parameters such as soil carbon storage.