Spatial variability of soil chemical properties of Moso bamboo forests of China

Abstract

This study investigates the spatial variability of soil organic matter (SOM), soil organic carbon (SOC) and pH in the upper 20-cm layer and 20–40 cm layer in Moso bamboo (Phyllostachys pubescens Pradelle) forests using a geostatistics model. Interpolation maps of SOM, SOC, and pH were developed using ordinary kriging (OK) and inverse distance weighted (IDW) methods. The pH, SOC, and SOM of the two soil layers ranged from 4.6 to 4.7, from 1.5 to 2.7 g kg⁻¹ and from 20.3 to 22.4 g kg⁻¹, respectively. The coefficient of variation for SOM and SOC was 29.9–43.3% while a weak variability was found for pH. Gaussian and exponential models performed well in describing the spatial variability of SOC contents with R² varying from 0.95 to 0.90. The nugget/sill values of pH are less than 25%, which indicates a strong spatial correlation, while the nugget/sill values of SOC and SOM fall under moderate spatial correlation. Interpolation using ordinary kriging and inverse distance weighted methods revealed that the spatial distribution of SOM, SOC, and pH was inconsistent due to external and internal factors across the plots. Regarding the cross-validation results, the ordinary kriging method performed better than inverse distance weighted method for selected soil properties. This study suggests that the spatial variability of soil chemical properties revealed by geostatistics modeling will help decision-makers improve the management of soil properties.

Keywords

Cross-validation / Geostatistics / Inverse distance weighted / Ordinary kriging / Semi-variance

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