Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China

To obtain accurate spatial distribution maps of soil organic carbon (SOC) and total nitrogen (TN) in the Hetian Town in Fujian Province, China, soil samples from three depths (0–20, 20–40, and 40–60 cm) at 59 sampling sites were sampled by using traditional analysis and geostatistical approach. The...

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Veröffentlicht in:Journal of forestry research 2020-12, Vol.31 (6), p.2385-2394
Hauptverfasser: Yao, Xiong, Yu, Kunyong, Deng, Yangbo, Liu, Jian, Lai, Zhuangjie
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Sprache:eng
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Zusammenfassung:To obtain accurate spatial distribution maps of soil organic carbon (SOC) and total nitrogen (TN) in the Hetian Town in Fujian Province, China, soil samples from three depths (0–20, 20–40, and 40–60 cm) at 59 sampling sites were sampled by using traditional analysis and geostatistical approach. The SOC and TN ranged from 2.26 to 47.54 g kg −1 , and from 0.28 to 2.71 g kg −1 , respectively. The coefficient of variation for SOC and TN was moderate at 49.02–55.87% for all depths. According to the nugget-to-sill ratio values, a moderate spatial dependence of SOC content and a strong spatial dependence of TN content were found in different soil depths, demonstrating that SOC content was affected by both extrinsic and intrinsic factors while TN content was mainly influenced by intrinsic factors. Indices of cross-validation, such as mean error, mean standardized error, were close to zero, indicating that ordinary kriging interpolation is a reliable method to predict the spatial distribution of SOC and TN in different soil depths. Interpolation using ordinary kriging indicated the spatial pattern of SOC and TN were characterized by higher in the periphery and lower in the middle. To improve the accuracy of spatial interpolation for soil properties, it is necessary and important to incorporate a probabilistic and machine learning methods in the future study.
ISSN:1007-662X
1993-0607
DOI:10.1007/s11676-019-01014-8