This study aimed to establish a soil quality index (SQI) using factor analysis (FA) and network analysis (NA) techniques for the assessment of soil quality after deforestation in Northern Iran. In this work, 16 soil properties from forest sites and adjacent cropland sites at two locations in the Hyrcanian forests of Northern Iran were used and analyzed. The minimum data set (MDS) indicators selected through the FA method were potential carbon mineralization (PCM), urease activity (URE), plant-available water, and cation exchange capacity, which contributed to the SQI value by 39, 28, 19, and 13%, respectively. Soil quality indicators identified through the NA approach were URE, organic C (OC), PCM, and microbial biomass C (MBC), contributing to the SQI value by 34, 26, 23, and 17%, respectively. The PCM and URE, selected by both techniques, were the most significant indicators of soil quality to detect the deforestation impacts in Northern Iran. The computed SQIs were significantly correlated with OC stocks, validating the SQI models developed by both FA and NA methods. The SQI values computed through NA were more sensitive to deforestation than those computed through FA, suggesting the NA-screened SQI would represent changes in soil functions more adequately than the FA-screened SQI. Croplands were characterized by a lower value of SQI (62–79%), especially in the surface layer, indicating a loss of soil capacity to function well after deforestation for the expansion of farmlands. The current study shows that the SQI model developed by the NA approach would also be a useful, alternative technique for soil quality assessment after the conversion of native forest ecosystems to agricultural lands. To conclude, our results confirmed a weighted correlation-based NA of the soil attributes can be used as a simple and robust tool for assessing soil quality following deforestation and loss of ecosystem services.
Evaluating forest soil quality after deforestation and loss of ecosystem services using network analysis and factor analysis techniques
Year: 2022
