Study area
The study was conducted across the Loess Plateau (33°43′–41°16′N, 100°54′–114°33′E) (Fig. 1a), which represents approximately 6.5% of the total area of China6. The study area is dominated by temperate, arid, and semiarid continental monsoon climates. The annual evaporation is 1400–2000 mm, and the annual temperature ranges from 3.6 °C in the northwest to 14.3 °C in the southeast on the Loess Plateau7, while the annual precipitation ranges from 150 to 800 mm, where 55–78% of the precipitation falls from June to September7. The annual solar radiation ranges from 5.0 × 109 to 6.7 × 109 J m−2. The vegetation zones are forest, forest-steppe, typical-steppe, desert-steppe, and steppe-desert zones8 from southeast to northwest.
Locations of the Loess Plateau region in China (a) and the sampling sites (b); image data processed by ArcGIS 10.5 http://developers.arcgis.com.
Field sampling
According to the different climate zones and vegetation types, five classic sampling sites were selected (Fig. 1b) on the Loess Plateau, which were Yangling, Changwu, Fuxian, Ansai, and Shenmu from south to north. Drilling equipment (assembled by Xi’an Qinyan Drilling Co. Ltd, China) was used to collect soil samples from soil surface down to bedrock. At each sampling site, disturbed soil samples were collected to determine the SAP and SAK concentrations, pH, soil particle composition, and soil organic matter contents. In addition, disturbed soil samples were collected from the middle of the soil column at 1-m intervals (i.e., 0.5 m, 1.5 m, 2.5 m, 3.5 m, etc.). The drilling and sampling work was carried out from April 28 to June 28, 2016. The total numbers of disturbed soil samples collected from Yangling, Changwu, Fuxian, Ansai, and Shenmu were 103, 205, 181, 161, and 58, respectively, and the corresponding soil drilling depths were 103.5 m, 204.5 m, 187.5 m, 161.6 m, and 56.6 m, respectively.
Laboratory analyses
Undisturbed soil samples were air-dried, separated, and passed through 0.25-mm or 2-mm sieves. SAP and SAK were extracted with ammonium lactate solution and detected by spectrophotometry and flame photometry. Soil total nitrogen (STN) concentrations were determined by the Kjeldahl digestion procedure9. Soil total phosphorus (STP) concentrations were determined by molybdenum antimony blue colorimetry10. The soil organic carbon (SOC) contents were analyzed by dichromate oxidation method11. The soil particle composition was determined by laser diffraction (Mastersizer 2000, Malvern Instruments, Malvern, UK)12. According to the mixture of soil and water mass ratio of 1:1, the pH value was determined with a pH meter equipped with a calibrated combined glass electrode. The soil water content (SWC) was determined by the mass loss after drying to constant mass in an oven at 105 °C13. The calcium carbonate content was determined by the acid-neutralization method14.
Geostatistical analysis
The geostatistical analysis was chosen to determine the spatial structure of the spatially dependent soil properties15, where a semivariogram was employed to quantify the spatial patterns of the variables. The equation for the semivariogram is16:
$$ {text{R}}left( {text{h}} right) , = frac{1}{{2{text{N}}left( {text{h}} right)}}mathop sum limits_{{{text{i}} = 1}}^{{{text{N}}left( {text{h}} right)}} left[ {{text{Z}}left( {{text{x}}_{{text{i}}} } right){-}{text{Z}}left( {{text{x}}_{{{text{i}} + {text{h}}}} } right)} right]^{{2}} , $$
(1)
where for each site i, N(h) is the number of pairs separated by h, and Z(xi) is the value at location xi and Z(xi+h) for xi+h. There are four semivariogram models (spherical, exponential, linear, and Gaussian), which can be employed to describe the semivariogram, and the best fitting model is selected according to the smallest residual sum of squares (RSS) and the largest coefficient of determination (R2). The equation of each semivariogram model is16:
Exponential model:
$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + {text{C}}left[ {({1}{-}{text{exp}}( – {text{h}}/{text{A}}_{0} )} right] $$
(2)
Linear Model:
$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + left[ {{text{h}}left( {{text{C}}/{text{A}}_{0} } right)} right] $$
(3)
Spherical Model:
$$ {text{R}}left( {text{h}} right) = {text{C}}0 + {text{C}}left[ {{1}.{5}left( {{text{h}}/{text{A}}_{0} } right) – 0.{5}left( {{text{h}}/{text{A}}_{0} } right)^{{3}} } right] ;;;;;;;;; {text{h}} le {text{A}}0 $$
(4)
$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + {text{C}};;;;;;;;;;{text{h}} ge {text{A}}0 $$
(5)
Gaussian Model:
$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + {text{C}}left[ {{1} – {text{exp}}left( { – {text{h}}^{{2}} /{text{A}}_{0}^{{2}} } right)} right] $$
(6)
where C0 indicates the nugget value, which is the short-range structure that occurs at distances smaller than the sampling interval, microheterogeneity, and experimental error; C0 + C is the sill indicating the random and structural variation, and; A0 is the range indicating the spatial correlation at different distances.
Statistical analysis
Descriptive statistical analyses (maximum, minimum, average, and coefficient of variation), Pearson’s correlation analysis, and linear regression analysis was performed with SPSS 16.0 (IBM SPSS, Chicago, IL, USA). Geostatistical analysis was performed with GS + software (version 7.05).
Source: Ecology - nature.com
