Date Published: October 18, 2018
Publisher: Public Library of Science
Author(s): Xinping Zhang, Fangfang Zhang, Dexiang Wang, Junxi Fan, Youning Hu, Haibin Kang, Mingjie Chang, Yue Pang, Yang Yang, Yang Feng, Suzannah Rutherford.
Although the spatial mapping and fertility assessment of soil chemical properties (SCPs) are well studied in the Loess Plateau region of China at farmland scale, little is known about spatial mapping the SCPs and their fertility and their influence factors at urban forest scale. The objectives of this study were to (1) compare the performance of two spatial interpolation methods, Ordinary kriging (OK) and regression kriging (RK), and (2) explain the relationships of the vegetation, terrain, and soil layer depth between the eight SCPs and their fertility, and (3) find the limiting factors of soil comprehensive fertility in this study area? The Yan’an urban forest was taken as study case, used hybrid spatial interpolation methods based on OK and RK to mapping eight SCPs and the soil fertility in each soil layer (0–20 cm, 20–40 cm, and 40–60 cm) for 285 soil samples. The results indicated that RK outperformed OK for total nitrogen (TN), available potassium (AK), organic matter (OM) in 0–60 cm profile and available phosphorus (AP) in the 0–20 cm and 40–60 cm soil layers because RK considered the impact of terrain. The terrain factors, comprising the relative terrain position, slope, aspect, and relative elevation significantly affected the SCPs and spatial heterogeneity of fertility, where the vegetation cover types determined the average SCPs to some extent. On average, the six SCPs (except total potassium and AP) and the fertility decreased as the soil layer depth increased. Ten vegetation cover types comprising broadleaved mixed natural forest (BM), cultivated land (CL), economic forest (EF), grassland (GL), Platycladus orientalis natural forest (PON), Platycladus orientalis plantation (POP), Pinus tabuliformis plantation (PT), Quercus wutaishanica natural forest (QW), Robinia pseudoacacia plantation (RP), and Shrubwood (SW) were associated with significant differences in TN, OM, AN, AP, and AK, across the three soil layers. QW, PON, and BM also had higher content of TN, OM, AN, and AK contents than the other vegetation cover types. There were small differences in TK, AK, and pH among the 10 vegetation cover types. We concluded that AN, TN, and OM are the limiting factors of soil comprehensive fertility in this region. These results improve understanding of the spatial mapping, influence and limiting factors of SCPs and their fertility at urban forest scales.
Soil plays an essential role in the biosphere by governing plant productivity, organic matter (OM) degradation, and nutrient cycles . Soil fertility is one of the major drivers of ecological processes, and thus it is frequently investigated in ecological research . The eight soil chemical properties (SCPs), comprising total nitrogen (TN), total potassium (TK), total potassium (TP), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and organic matter (OM) are important chemical components of soil fertility. Hence, spatially continuous mapping of these soil chemical properties and soil fertility is required to facilitate the sustainable management of land resources in precision agriculture and forestry [3–5], while it is also helpful to understand the belowground food webs , plant species distributions , and other factors. However, abundant observations of soil properties cannot always be obtained easily across a large landscape because of cost and time constraints on soil sampling and analysis . Therefore, spatial interpolation is commonly used to generate soil property maps from discrete point-based data . Previous studies have shown that auxiliary variables are important for predicting soil properties . In recent years, the availability of high-resolution topographical data has provided ancillary variables for accurately mapping soil chemical properties [11–14]. The interpolation method employed critically affects the accuracy of interpolation. Among the existing interpolation techniques, the two commonly used methods are ordinary kriging (OK) [3,12,15,16] and regression kriging (RK) [8,16–19]. RK performs better than OK because its uses auxiliary variables, as well as reducing the number of observations needed for target variables . Previous studies have investigated the effects of topography and the dominant trees species on the spatial distribution of soil physicochemical properties [3,12,21–23]. Terrain is one of the main factors that affect the soil C, N, and P contents at the landscape scale [3,21,24]. In recent decades, many studies have shown that a geostatistical approach based on the integration of terrain factors is an effective tool for accurately predicting the spatial distribution of soil chemical properties [3,15,21,25–29]. Fraterrigo et al.  showed that vegetation cover types have persistent, long-term effects on the spatial heterogeneity of soil resources, which may not be detectable when the values are equalized across sites. Differences in the distribution and supply of soil chemical properties could alter the composition and diversity of forest ecosystems by interacting with the patterns of variability in the plant and heterotrophic organisms. These activities may continue to influence the distributions of soil nutrients by altering their spatial heterogeneity in ecologically sensitive regions, such as, the Loess Plateau in China (LPC).
This case study based on the spatial mapping and the variations in the eight soil chemical properties (TN, TK, TP, AN, AP, AK, OM, and pH) as well as the soil fertility in the hilly gully Loess Plateau at the urban forest scale obtained three main findings. First, the majority of the soil chemical properties exhibited moderate spatial dependencies and they were suitable for ordinary kriging interpolation, whereas others with weak spatial dependencies required regression kriging interpolation with topographic factors (elevation, slope, aspect, the sin of aspect, relative position index, etc.) as auxiliary variables. Second, the concentrations of the eight soil chemical properties were significantly influenced by the vegetation cover types due to differences in TNPS and DBH at the sample-plot scale. Third, the AN, TN, and OM were the limiting factors in our study area, and which could be improved by natural broad-leaved forests (Q.wutaishanica forests, Betula platyphylla forests, etc.).