The quantification of the pattern and spatial distribution of soil organic carbon （SOC） is fundamental to understand many ecosystem processes. This study aimed to apply ordinary kriging （OK） to model the spatial d...The quantification of the pattern and spatial distribution of soil organic carbon （SOC） is fundamental to understand many ecosystem processes. This study aimed to apply ordinary kriging （OK） to model the spatial distribution of SOC in a selected part of Zambia. A total of 100 soil samples were collected from the study area and analyzed for SOC by determining soil oxidizable carbon using the Walkley-Black method. An automated fitting procedure was followed when modeling the spatial structure of the SOC data with the exponential semivariogram. The results indicated that the short range spatial dependence of SOC was strong with a nugget close to zero. The spatial autocorrelation was high to medium with a nugget to sill ratio of 0.25. The root mean square error of the predictions was 0.64, which represented 58.18% of the mean observed data for SOC. It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported. Therefore, the maps produced can be used as guides for various uses including optimization of soil sarapling.展开更多
基金partially supported in finance by the Ministry of Education, Science and Vocational Training and Early Education, Zambia
文摘The quantification of the pattern and spatial distribution of soil organic carbon （SOC） is fundamental to understand many ecosystem processes. This study aimed to apply ordinary kriging （OK） to model the spatial distribution of SOC in a selected part of Zambia. A total of 100 soil samples were collected from the study area and analyzed for SOC by determining soil oxidizable carbon using the Walkley-Black method. An automated fitting procedure was followed when modeling the spatial structure of the SOC data with the exponential semivariogram. The results indicated that the short range spatial dependence of SOC was strong with a nugget close to zero. The spatial autocorrelation was high to medium with a nugget to sill ratio of 0.25. The root mean square error of the predictions was 0.64, which represented 58.18% of the mean observed data for SOC. It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported. Therefore, the maps produced can be used as guides for various uses including optimization of soil sarapling.
