Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques

Soil degradation reduces soil quality by a loss of organic matter, reduced soil fertility, and structural breakdown, erosion, undesirable changes in salinity, acidity or alkalinity and the effects of toxic chemicals, contaminants or excessive flooding. In this study, we investigated spatial and temporal changes of soil quality between 1986 and 2030 in the Central Iranian desert. 201 topsoil samples (0-20 cm) in Yazd-Ardakan plain of Iran were chosen with conditioned Latin hypercube sampling strategy and analysed for soil organic carbon, electrical conductivity, dry bulk density, aggregate stability, and soil heavy metals. A soil quality index was calculated using weighted index method. To predict the spatial distribution of the soil quality index, we implemented a random forest model based on a set of covariates with a coefficient of determination of 0.69 between soil quality index and covariates. The results of soil quality index changes from 1986 to 2030 show that during this period, the areal extent of the Very Low SQI Class (< 0.4) increased by 78,242 ha (increase of 16.2%), while the extent of the Good SQI Class (> 0.6) decreased by 366,018 ha (decrease of 75.8%). This indicates that the soil quality in the study area, over time, is progressively becoming poorer. The results of the study of soil quality changes can be used in land evaluation, environmental studies and integrated planning and management in order to properly and reasonably utilize natural resources and reduce future soil degradation.