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These predictions were then used to predict soil organic carbon content.
Reflectance spectroscopy has demonstrated its ability to rapidly predict soil physio-chemical properties; however prediction accuracy varies among soil properties.
Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes.
To predict soil maps, data-mining techniques can be utilised.
The aim is to reproduce the original map and predict soil distribution in the adherent landscape.
Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth.
It was also shown that DEM can be used to accurately predict soil failure planes.
Novel modified RK methods are proposed for further investigation to predict soil properties and classes.
However, their ability to predict soil moisture is found to be more diverging.
Mechanistic water balance models can be used to predict soil moisture dynamics and drought stress in individual forest stands.
The results showed that the Taguchi method could predict soil erosion with an average relative error of 12.4%.
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