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Vertical distribution of STN and STP in watershed of loess hilly region

  • Tingting Meng , Jinbao Liu EMAIL logo , Huanyuan Wang and Yichun Du
Published/Copyright: May 24, 2022
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Open Geosciences
From the journal Open Geosciences Volume 14 Issue 1

Abstract

In order to explore the effects of land use change on the contents of total nitrogen and total phosphorus in deep soil, four land use types (cropland, grassland (7 years), grassland (30 years), and Jujube orchard) were selected from the Yuanzegou watershed in the loess hilly region of northern China. Soil samples at 0–10 m depth were collected to measure the contents of soil total nitrogen (STN) and soil total phosphorus (STP), and their stocks were estimated. The results showed that the STN content showed a decreasing trend with the increase in soil depth, and the lowest STN content of grassland (7 years) was 0.09–0.17 g kg−1. The range of STN content in the watershed was 0.12–0.22 g kg−1 and the coefficient of variation was 10.52–25.90%, which belonged to medium variation. The STP content is stable regionally with the change in soil depth, and does not change much (except for grassland [30 years]). STP content of the watershed is 0.81–1.05 g kg−1 and the coefficient of variation is 9.37–54.69%, which is a high variation. The change trend of STN and STP stocks is consistent with the nitrogen and phosphorus content. The results revealed the dynamic changes in STN and STP after land use change, and estimated the stocks of STN and STP in deep loess, which provided scientific basis for land and soil resource management and sustainable development of the project of returning farmland to forest or grassland in small watershed of loess hilly-gully region.

Keywords: Yuanzegou watershed; nitrogen; phosphorus; deep soils; land use

1 Introduction

Soil total nitrogen (STN) and phosphorus (STP) are two major elements influencing both plant growth and global biogeochemical cycles [1,2]. In terrestrial ecosystems, STN and STP play important roles by affecting soil properties [3], plant growth [4,5], and soil microbial activities [6]. In agricultural ecosystems, STN and STP are the major determinants and indicators of soil quality, which are closely related to soil productivity. The reduction in STN and STP levels can result in a decrease in soil nutrient supply, fertility, porosity, penetrability, and, consequently, in soil productivity [7]. In environmental science, nitrogen and phosphorus are the main non-point source pollutants of surface water and ground water. In addition, STN and STP are closely correlated to soil organic carbon (SOC) cycle [8], and they have dynamic effects on greenhouse gas emissions, which are linked to global climate change [9]. Thus, it is of great significance to scientifically evaluate the dynamic changes in N and P for optimizing land management, ecological environment health, and food security [10,11].

The Loess Plateau of North China is famous for its deep loess, unique landscapes, and intense soil erosion [12]. The annual sediment input from the loess plateau of Shaanxi province to the Yellow River is 7.67 t, accounting for 48% of the annual sediment transport volume of the Yellow River [13]. Excessive soil and water losses on Loess Plateau cause serious nutrient losses, soil degradation, and reduction in crop yields, which significantly restrict the development of the regional economy and rise of the living standard for the farmers. In 1999, a large-scale ecological engineering program called “Grain for Green” was initiated to control serious soil erosion there by the central government of China [14]. Since then, the type of land use has changed.

The change in land use type is accompanied by the change in vegetation. Vegetation, as an important part of terrestrial ecosystem, is interdependent and mutually restricted between vegetation and soil in the ecosystem [15]. The direction and speed of vegetation occurrence, development, and succession will be affected by the physical and chemical properties of the soil. In turn, soil physical and chemical properties will change with the evolution of plant communities [16,17]. Human-induced land use change has been identified as one of the major factors that affect soil C, N, and P cycles because it may alter plant species, land management practice, and soil microbial community structure [18,19,20,21,22].

There are many studies on the effects of land use change on soil nutrient distribution in loess hilly region. Wang et al. [2] and Liu et al. [23] used geostatistical methods to study the spatial heterogeneity of STN and STP in about 700 soil samples under 0–40 cm depth of different land use types across the entire Loess Plateau region of China. Wei et al. [24] studied the distribution characteristics of STN and STP in the soil at a depth of 0–40 cm under three adjacent soil use types in the northern Loess Plateau. Xue et al. [25] studied STN, nitrate nitrogen, and ammonium nitrogen in 0–60 cm soil of different land use types in the hilly and gully region of the Loess Plateau in Ningxia. Qiao et al. [26] studied the vertical distribution characteristics of STN and STP in 0–200 m deep soil from 703 soil samples in 5 regions of the Loess Plateau, but did not study the vertical distribution characteristics of STN and STP under different land uses. In summary, previous studies on STN and STP under different land uses on the Loess Plateau were mainly concentrated in the shallow soil layer, and the research on the vertical distribution of STN and STP in deep soil was limited to different regions of the Loess Plateau. Therefore, the research on the vertical distribution of STN and STP in deep soil under different land use types on the Loess Plateau is still relatively scarce.

Determining the vertical distributions of STN and STP as well as the factors that influence them under different land use can help evaluate the impact of patterns of land utilization conversion on soil N and P reserves [27,28]. In previous studies, researchers mainly focused on the effects of land use change on dynamics of C, N, and P in topsoil (≤30 cm) because this soil layer stores high levels of C, N, and P, which can be easily influenced by external disturbance [29,30]. However, due to limited annual precipitation (<600 mm) and thick loess soils on the loess plateau, perennial grass and forest species that have established during the last three decades in the region can extend their roots deeper than 10 m into soils, thereby modifying the water, carbon, and nitrogen cycling [2]. Thus, the objectives of this study were (1) to investigate the vertical distributions of STN and STP with different land use types; (2) to analyze the factors that influence STN and STP; and (3) to evaluate the vertical distribution of the STN and STP stocks in deep soils.

2 Methods

2.1 Study area

The experimental area is located in the small watershed of Yuanzegou (37°150′N, 110°210′E) in the loess hilly region of northern China. The basin has deep steep gully, deep loess soil, and serious erosion, covering an area of about 0.58km2, among which the gully area accounts for half of the total area of the sub-basin. The region has an arid and semi-arid climate, with an average annual precipitation of about 498 mm, 65% of which is mainly in autumn. This terrain is composed of complex features: gullied slopes of hills (20° < mostly with gradients < 45°) in upper parts and deep, the loess soils are typical silt loams belonging to Inceptisols (United States Department of Agriculture), usually with >50% silt contents and <30% clay contents. Since the implementation of the conversion of farmland to forest, the main land use types in the basin include cropland, 7-year grassland, 30-year grassland, and jujube orchard (as shown in Figure 1).

Figure 1 Different land use types in the Yuanzegou watershed.
Figure 1

Different land use types in the Yuanzegou watershed.

2.2 Soil sampling and laboratory analysis

In August 2015, according to the area size and topographic factor (Table 1) of each land use type, 5, 4, 8, and 4 sample points were collected with soil drills on small watershed cropland, 7-year grassland, 30-year grassland, and jujube forest, respectively, with a sampling depth of 0–10 m and 10 layers in total, according to the random sampling method. Some of the soil samples were collected and put into an aluminum box to determine the soil moisture content [31], and some were put into a self-sealed bag and taken back to the laboratory for later use. The soil samples brought back to the laboratory were air-dried for 7 days and then passed through 2 and 0.25 mm sieve, respectively. The 2 mm soil sample was used to determine soil particle composition [32] and the 0.25 mm soil sample was used to determine SOC, STN, and STP, The determination method of carbon, nitrogen, and phosphorus was conventional experimental method [33]. Soil properties in watershed are shown in Table 2.

Table 1

Topographic factor for each land use

Land uses Slope Altitude Aspect Vegetation types
Cropland 23.86 987.82 248.90 Setaria italica and Zea mays
Grassland (7 years) 31.32 982.85 290.33 Stipa bungeana Trin. and Artemisia scoparia
Grassland (30 years) 20.80 1061.62 229.79 Tripolium
Jujube orchard 28.55 1011.94 328.22 Ziziphus jujube Mill.
Table 2

Soil properties with the depth of the soils under each land use

Land uses Soil depth Bulk density Soil moisture SOC Soil texture fractions
(m) (g cm−3) (%) (g kg−1) Clay (%) Silt (%) Sand (%)
Cropland 1 1.21 14.94 2.21 14.92 72.08 12.99
2 1.28 13.48 1.77 15.58 72.20 12.23
3 1.34 12.90 1.68 14.05 69.63 16.32
4 1.31 13.80 1.55 14.77 72.70 12.53
5 1.33 13.61 1.55 13.23 67.63 19.14
6 1.22 13.71 1.50 18.24 61.65 20.11
7 1.32 13.75 1.48 12.06 64.45 23.49
8 1.28 14.10 1.40 15.73 68.74 15.54
9 1.23 13.82 1.31 12.45 62.06 25.49
10 1.33 13.93 1.23 11.27 58.80 29.92
Grassland (7 years) 1 1.30 11.91 1.20 15.05 69.29 15.66
2 1.34 10.85 0.89 16.43 64.89 18.67
3 1.32 12.23 1.52 15.96 63.33 20.70
4 1.35 12.78 1.30 18.99 67.55 13.45
5 1.20 11.29 0.85 15.96 64.83 19.21
6 1.23 11.20 0.96 13.17 59.91 26.92
7 1.33 11.84 1.33 15.92 66.44 17.64
8 1.21 12.86 1.12 20.40 67.25 12.36
9 1.22 12.24 1.03 18.71 65.08 16.21
10 1.31 13.33 1.01 17.89 70.72 11.38
Grassland (30 years) 1 1.30 12.02 2.57 15.90 71.90 12.20
2 1.38 13.29 2.23 15.95 69.75 11.12
3 1.39 12.62 1.78 15.32 61.50 23.18
4 1.40 12.11 1.69 14.59 70.87 14.55
5 1.37 11.89 1.36 13.27 67.62 19.11
6 1.40 12.81 1.44 13.26 68.00 18.74
7 1.31 13.17 1.52 16.35 73.39 10.26
8 1.32 13.30 1.46 12.16 42.94 44.90
9 1.27 13.28 0.65 23.67 67.40 8.93
10 1.32 12.72 0.76 15.89 61.53 22.59
Jujube orchard 1 1.20 11.89 2.50 12.46 60.88 26.65
2 1.19 11.18 2.36 13.70 66.80 13.26
3 1.04 11.67 2.16 16.26 62.65 21.08
4 1.22 10.98 1.80 15.56 69.45 14.98
5 1.27 10.40 1.56 11.27 57.43 13.69
6 1.23 11.23 1.33 13.22 67.18 19.59
7 1.32 8.97 1.59 13.56 67.10 19.34
8 1.25 10.22 0.73 5.65 28.89 11.40
9 1.31 9.94 0.80 16.05 68.39 15.56
10 1.25 9.54 0.53 19.51 69.44 11.05

2.3 Calculations

The stocks of STN and STP were calculated using the following equations [34]:

STNS i = D i ⁎ BD i ⁎ STNC i ⁎ 1 100 ,

STPS i = D i ⁎ BD i ⁎ STPC i ⁎ 1 100 ,

where STNS and STPS are the stocks of STN and STP (kg m−2), respectively, i is the ith soil layer, D is the soil layer thickness (cm), BD is the bulk density (g cm−3), and STNC and STPC are the concentrations of STN and STP (g kg−1), respectively. While for lower layers, the soil bulk density was estimated using the following pedotransfer function (PTF) developed by Wang et al. [35] from 1,300 Loess Plateau datasets:

BD i = 1.8284 + 0.0429 × log 10 ( clay i ) + 0.0205 × clay i 0.5 − 0.0125 × cos ( clay i ) − 0.0061 × silt i + 0.0001 × silt i × SG i − 0.0098 × SG i − 0.0071 × SOC i − 0.0505 × SOC i 0.5 + 0.0002 × SOC i 2 ,

where clay and silt are contents at the ith depth, respectively; SG is slope gradient at the sample location; and SOC is contents at the ith depth.

2.4 Statistical analysis

Excel 2020 and SPSS 22.0 software were used for statistical analysis of the data. The CV was used to represent the overall variation in STN, STP, STNS, and STPS. Pearson correlation coefficient indicates the strength of possible relationships between STN, STP, and other soil properties, and Origin2018 was used for mapping.

3 Results

3.1 Vertical distribution of STN and STP

Vertical distribution of STN and STP contents on the 0–10 m profile for different land use types in small watershed is shown in Figure 2(a) and (b). The STN content of the four land use types decreased with the increase in soil depth. The STN content of cropland, grassland (7 years), grassland (30years), and Jujube orchard are 0.12–0.21, 0.09–0.17, 0.09–0.26, and 0.14–0.22 g kg−1. The STN content of grassland (7 years) was lower than that of the other 3 land use types (Figure 2a). Except for grassland (30 years), STP content tended to be stable with the increase in soil depth, but changed little, in cropland, grassland (7 years), and Jujube orchard with the values being 0.96–1.21, 0.71–1.12, and 0.77–1.17 g kg−1, respectively. The STP content of grassland (30 years) fluctuated greatly, and the range was 0.65–1.08 g kg−1. Especially in the 7 m soil layer, it decreased significantly, and there was little difference in STP content among the different land use types (Figure 2b).

Figure 2 STN (a) and STP (b) concentrations under different land uses in deep profiles (0–10 m). The error bar represents ± standard deviation.
Figure 2

STN (a) and STP (b) concentrations under different land uses in deep profiles (0–10 m). The error bar represents ± standard deviation.

Distribution of STN content and its coefficient of variation under four land use patterns in Yuanzegou small watershed is shown in Figure 3(a) and (b). The STN content of the four land use types ranged from 0.12 to 0.22 g kg−1 in the 0–10 m profile, with an average value of 0.15 g kg−1, and the variation coefficient ranged from 10.52 to 25.90%, showing moderate variation. STN content in the small watershed of Yuanzegou decreased with the increase in soil depth.

Figure 3 STN content (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STN content (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.
Figure 3

STN content (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STN content (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.

Distribution of STP content and its coefficient of variation under four land use patterns in Yuanzegou small watershed is shown in Figure 4(a) and (b). The STP content of the four land use types ranged from 0.81 to 1.05 g kg−1 in the 0–10 m profile, with an average value of 0.94 g kg−1, and the variation coefficient ranged from 9.37 to 54.69%, showing high variation. STP content in the small watershed of Yuanzegou increased first and then decreased with the increase in soil depth.

Figure 4 STP content (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STP content (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.
Figure 4

STP content (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STP content (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.

3.2 Correlation analysis

For the four land use types, the correlation between STN and STP contents in 0–10 m soil profiles and soil properties is shown in Table 3. Except for the Jujube orchard, the contents of STN and STP were weakly correlated with bulk density and soil moisture. STN content was negatively correlated with sand content (r = –0.44, –0.47, –0.34, –0.48, p < 0.01) and positively correlated with organic carbon content (r = 0.47, 0.53, 0.51, 0.55, p < 0.01） in four land use types. STP content was negatively correlated with clay content (r = –0.41, –0.38, –0.37, –0.37, p < 0.01) and weakly correlated with organic carbon content in the four land use types. There was no significant correlation between STN and STP in the four land use types.

Table 3

Pearson’s correlation coefficients between STN, STP, and selected soil properties

Land uses Variable STN (g kg−1) STP (g kg−1) BD (g cm−3) Sand (%) Silt (%) Clay (%) SM (%) SOC (g kg−1)
Cropland STN 1 0.13 0.09 –0.44** 0.41** 0.38** 0.12 0.47**
STP 0.13 1 –0.02 0.1 0.22* –0.41** 0.08 0.20
Grassland (7 years) STN 1 –0.05 0.11 –0.47** 0.21* 0.05 0.19 0.53**
STP –0.05 1 0.07 0.23* 0.39** –0.38** –0.11 0.07
Grassland (30 years) STN 1 –0.07 0.11 –0.34** 0.45** –0.08 –0.07 0.51**
STP –0.07 1 0.07 –0.08 –0.07 –0.37** 0.11 –0.04
Jujube orchard STN 1 0.09 0.06 –0.48** –0.28* 0.12 –0.20 0.55**
STP 0.09 1 0.05 –0.14* 0.31* –0.37** –0.33* 0.12

BD, Bulk density; SOC, soil organic carbon; SM, soil moisture. *Significant correlations at the 0.05 probability level (two-tailed). **Significant correlations at the 0.01 probability level (two-tailed).

3.3 Vertical distribution of STNS and STPS

Vertical distribution of STN and STP stocks on the 0–10 m profile for different land use types in small watershed is shown in Figure 5(a) and (b). Similar to STN, the STP stocks of the four land use types decreased with the increase in soil depth. The STN stocks of cropland, grassland (7 years), grassland (30 years), and Jujube orchard are 0.17–0.29, 0.13–0.24, 0.13–0.36, and 0.20–0.31 kg m−2. The STN stocks of grassland (7 years) was lower than that of the other 3 land use types (Figure 5a). Except for grassland (30 years), STP stocks tended to be stable with the increase in soil depth, but changed little, in cropland, grassland (7 years), and Jujube orchard with the values being 1.35–1.58, 1.00–1.60, and 1.10–1.65 kg m−2, respectively. The STP content of grassland (30 years) fluctuated greatly, and the range was 0.03–1.50 kg m−2. Especially in the 7 m soil layer, it decreased significantly, and there was little difference in STP stocks among different land use types (Figure 5b).

Figure 5 STN and STP stocks under different land uses in deep profiles (0–10 m). The error bar represents ± standard deviation.
Figure 5

STN and STP stocks under different land uses in deep profiles (0–10 m). The error bar represents ± standard deviation.

Distribution of STN stocks and its coefficient of variation under four land use patterns in Yuanzegou small watershed is shown in Figure 6(a) and (b). The STN stocks of the four land use types ranged from 0.16 to 0.26 kg m−2 in the 0–10 m profile, with an average value of 0.21 kg m−2, and the variation coefficient ranged from 10.52 to 25.90%, showing moderate variation. The STN stocks in the small watershed of Yuanzegou decreased with the increase in soil depth.

Figure 6 STN stocks (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STN stocks (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.
Figure 6

STN stocks (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STN stocks (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.

Distribution of STP stocks and its coefficient of variation under four land use patterns in Yuanzegou small watershed is shown in Figure 7(a) and (b). The STP stocks of the four land use types ranged from 1.18 to 1.37 kg m−2 in the 0–10 m profile, with an average value of 1.31 kg m−2, and the variation coefficient ranged from 9.37 to 54.69%, showing high variation. The STP stocks in the small watershed of Yuanzegou increased first and then decreased with the increase in soil depth.

Figure 7 STP stocks (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STP stocks (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.
Figure 7

STP stocks (gray circles) and mean values (blue squares) (a) and the coefficient of variation for STP stocks (b) of soil profile in four land uses on the watershed. The dashed red line is the mean value of all the data for the entire 10 m profile. The error bars indicate the standard deviation.

4 Discussion

Accordingly, in this study we found higher STN contents and stocks under the grassland (30 years) and Jujube orchard than under cropland in the shallower layer. This result is consistent with other studies in the loess hilly region, where they found that cropland had lower STN content than forest land and grassland [36,37]. However, the STP content and stocks do not change much in the land use types. This result is similar to that in the study by Zaimes [38], that is, the change in land use type has little effect on STP. The main reason is that the soil erosion in a Loess Plateau is serious, STP content is generally low, and the phosphorus supply capacity is poor; the phosphorus is mainly related to the formation of the parent material.

Generally, the higher surface STN and STP under grassland can be attributed to greater above-ground and below-ground biomass, much lower soil erosion during heavy rainstorms [23,39,40], slower mineralization of organic matter, and better soil aggregation [41]. The main vegetation of the grassland (30 years) is Artemisia Scoparia, a kind of herb of Artemisia, which has less vertical root system, but the horizontal root system is more developed. The litter layer on the surface has a large water holding capacity, which can effectively absorb the water falling to the surface, delay the flow velocity of the surface runoff, and increase the infiltration time. In addition to the effective increase in infiltration, the underground root layer can effectively improve the soil’s impact resistance and protect the surface soil nutrients. Therefore, STN and STP contents in the surface soil of the grassland (30 years) are higher. Although the above-ground biomass in Jujube orchard can be greater than under cropland, inputs of litter (decayed leaves and branches) into surface soils can be negligible because of clean-cultivation soil management practices.

Cropland is generally considered to have a lower soil nutrient content than other natural land types (landless grasslands) because the biomass on the cropland is continuously removed and the disturbance of the land accelerates the decomposition and loss of nutrients [42,43]. However, in our study, STN and STP contents and stocks under cropland are higher than that of grassland (7 years), which is due to the cultivation of corn, millet, and cultivation during management. The organic fertilizer contains a large amount of organic matter, which are infiltrated into the soil with rainwater, so that STN and STP are replenished, and since the slope of the agricultural land is above 15°, the soil is not disturbed by mechanical turning, and the soil nutrient loss is reduced. Grassland (7 years), because of the short time of abandoned land, the surface vegetation is sparse, and the erosion by rain is serious. In addition, at the early stage of vegetation restoration, vegetation growth needs to consume nutrients. Therefore, the contents and stocks of STN and STP in grassland (7 years) were lower than that of cropland.

In this study, the variation trend and coefficient of variation of STN storage and STP storage are consistent with the nitrogen and phosphorus content, mainly due to the small difference in soil bulk density in the loess hilly area. The STN content is greatly affected by the SOC content and sand content, which is consistent with the study by Wang et al. [2]. The STP content of the soil is greatly affected by the silt content, which is consistent with the study by Qiao et al. [26].

5 Conclusion

We investigated the vertical distribution of STN and STP at 0–10 m soil depth under four different land use types in the Yuanzegou small watershed in the loess hilly area. The contents of STN and STP in soil decreased with the increase in soil depth. There were significant differences in STN and STP content under different land use types. The stocks of STN and STP in deep soil are mainly affected by soil bulk density. The results provided scientific basis for land and soil resource management and sustainable development of the project of returning farmland to forest or grassland in small watershed of loess hilly-gully region.

Acknowledgments

This work was jointly supported by the Scientific Research Item of Shaanxi Provincial Land Engineering Construction Group (DJNY2022-21).

  1. Author contributions: Tingting Meng: writing – original draft, writing – review and editing, methodology, and formal analysis; Jinbao Liu: writing – original draft, formal analysis, visualization, and project administration. Huanyuan Wang and Yichun Du: methodology and analysis.

  2. Conflict of interest: Authors state no conflict of interest.

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Received: 2021-07-14
Revised: 2022-04-01
Accepted: 2022-04-27
Published Online: 2022-05-24

© 2022 Tingting Meng et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2022-0377
Keywords for this article
Yuanzegou watershed; nitrogen; phosphorus; deep soils; land use
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Study on observation system of seismic forward prospecting in tunnel: A case on tailrace tunnel of Wudongde hydropower station
  3. The behaviour of stress variation in sandy soil
  4. Research on the current situation of rural tourism in southern Fujian in China after the COVID-19 epidemic
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