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Article

Effects of Land-Use Types on Topsoil Physicochemical Properties in a Tropical Coastal Ecologically Fragile Zone of South China

by
Yuduan Ou
1,
Gerónimo Quiñónez-Barraza
2,* and
Chubiao Wang
3,*
1
College of Coastal Agricultural Sciences, Guangdong Ocean University, Zhanjiang 524088, China
2
Campo Experimental Valle del Guadiana, Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias (INIFAP), Durango 34170, Mexico
3
Research Institute of Fast-Growing Trees, Chinese Academy of Forestry (CAF), Zhanjiang 524022, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(6), 5484; https://doi.org/10.3390/su15065484
Submission received: 13 February 2023 / Revised: 14 March 2023 / Accepted: 14 March 2023 / Published: 20 March 2023
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Abstract

:
Understanding the effects of land use on soil structure and nutrients is important for soil and water conservation in an ecologically fragile zone. This study was carried out to examine the variability of physicochemical properties in three different land use types: abandoned land (AL), eucalyptus plantation (EP), and road lawn (RL) sites at soil depths of 0–10 and 10–20 cm in the Leizhou Peninsula, a tropical coastal ecologically fragile zone of South China. Soil physicochemical property patterns exhibited extremely significant differences among the three land uses (p < 0.001) at both soil depths. Soil nutrients, natural water content, and total porosity, from high to low, in the different land use types were RL, AL, and EP, while the bulk density, from high to low, was EP, AL, and RL. Soil total nitrogen, total phosphorus, total potassium, available potassium, exchangeable calcium, exchangeable magnesium, and natural water content exhibited significant differences (p < 0.05) among the three land use types at soil depths of 0–10 and 10–20 cm, while no significant changes were detected regarding soil organic carbon, available phosphorus, and total porosity. The correlation between physical and chemical properties at the 10–20 cm depth (R = 0.97, p < 0.001) was closer and more significant than that at the 0–10 cm depth (R = 0.95, p < 0.01). Overuse of land (EP) without a rest in the ecologically fragile zone leads to soil erosion and compaction. Compared with natural restoration (abandoned land), artificial restoration (road lawn) can improve soil nutrient and water status more quickly, but cannot modify the soil organic carbon and porosity in the short term.

1. Introduction

Soil property is often coupled with the concept of ecosystem services, since soil balances the multi-functionality of the ecosystem [1]. Parent material, climate, and geological history all affect soil properties at both the regional and continental scales [2]. On a local scale, land use is an integrator of numerous environmental attributes which not only have direct impacts on soil properties, but also influence many ecosystem processes, functions, and services [3,4,5]. Land-use practices can alter the local patchiness and distribution of soil properties by decoupling interactions with microclimate, microtopography, vegetation, and soil biota [6,7]. Understanding soil property variations across land uses will help clarify the impacts of human activities on soil quality and health, soil and water conservation, ecological degradation, and restoration [8,9,10].
Vegetation changes associated with land use play a key role in soil development due to their influence on nutrient cycling, hydrological processes, and soil erosion [11,12]. Compared with vegetation altered by human land use, soils underlying native or secondary vegetation generally feature ample litter cover, organic inputs, developing below-ground carbon pools, and abundant burrowing fauna [13,14]. Some studies have found higher soil nutrient levels under natural or secondary vegetation (forest, grassland) than under managed landcover (pasture, farmland) [3], other studies have found the opposite results [15]. These distinctions of soil nutrient levels between soils underlying native or secondary vegetation and managed landcover are caused by the complex interaction of biogeographic setting and the specifics of management [6,16,17].
Ecologically fragile areas are widely distributed in China [18,19]. To halt the degradation of ecosystems and improve the eco-environment, China brings the most ecologically fragile areas under the ecological conservation red lines, among which are coastal zones in South China, which involve the Leizhou Peninsula. The coastal zones are regarded as the ecosystems most vulnerable to climate change and anthropogenic impacts because of rapid population growth, predatory economic development, and uncontrolled land-use change [15]. An ecologically fragile area has the following characteristics: low ecosystem stability, inferior ability to maintain homeostasis from interference, vulnerability to ecosystem degradation, and difficult spontaneous recovery [18,20].
The Leizhou Peninsula was once covered by a tropical monsoon forest, but since 1954, the clearing of natural vegetation has accelerated because of deforestation, slash-and-burn agriculture, and the establishment of eucalyptus plantations [21,22,23,24]. Conversion of native or secondary forest to eucalyptus plantations has become popular in some tropical and subtropical regions, especially in some developing countries [25]. The area of eucalyptus afforestation in China is the third-largest in the world, after India and Brazil [26]. In South China, millions of hectares of both poor cropland and inefficient secondary forest have been converted to eucalyptus plantations [26]. Eucalyptus is growing rapidly in the Leizhou Peninsula, accounting for 66.9% of the total forests in 2014, causing controversy regarding the influence of soil water and fertilizer [25,27,28]. Abandoned lands around the eucalyptus plantations were undergoing secondary succession, which played an important role in soil and water conservation [29,30,31]. The development of urbanization brought the creation of a road network, and the maturity of road greening increases the proportion of lawns. Such simple community structure and low community productivity resulted in poor erosion resistance [32]. The expansion of the road network brought by urbanization, variation of land use, and road greening will certainly affect the environment in ecologically fragile area. Studies on soil properties caused by land-use change seldom consider the tropical coastal ecologically fragile zones, hoping to solve common problems across different geographic spaces and provide guidance for ecological restoration [8].
A full understanding of anthropogenic impacts on land degradation requires the assessment of land use regarding key soil physicochemical properties such as nutrients, bulk density, and water content. Accordingly, this study aimed to investigate changes in the topsoil physicochemical properties under different land uses (eucalyptus plantation, abandoned land, and road lawn) in the Leizhou Peninsula, a tropical coastal ecologically fragile zone in South China. The results obtained from this study will enhance our understanding of how the vegetation cover and anthropogenic activities jointly influence the dynamic changes in soil properties and provide implications for ecological restoration in tropical coastal ecologically fragile zones.

2. Materials and Methods

2.1. Study Area

The Leizhou Peninsula is in the most southern area of mainland China (Figure 1). It falls close to the northern boundary of the tropical zone and has a tropical monsoon climate. The mean annual temperature is 23–24 °C, with average January and July temperatures of 16 °C and 29 °C (i.e., mean monthly temperatures for the coldest and hottest months), respectively [21]. The mean annual precipitation is 1400~1700 mm, with a rainy season from May to September (average monthly rainfall over 160 mm), which is also the season for typhoons, while the dry season extends from October to April, with an average monthly rainfall of less than 50 mm [21].
The terrain is a relatively flat volcanic topography, made up of mainly platform and shore terrace areas [21]. A terrace of volcanic basalt stands mainly at a 10–50 m altitude, although some volcanic cones may reach 100–260 m [21]. Most of the Leizhou Peninsula is covered by Quaternary and Late Cenozoic basalts which are easily weathered, resulting in eluvial laterite formation [24]. The soil types are mainly Luvisols and Lithosols [33].

2.2. Site Selection

From the south to the north of the main road (Shugang Dadao) of the Mazhang District (21°06′29.8″~21°18′41.8″ N, 110°18′25.8″~110°17′48.7″ E), in the northeastern part of the Leizhou Peninsula, an approximate 23 km belt transect was set up, with 24 sampling sites and 3 land uses, including abandoned land (AL), eucalyptus plantation (EP), and road lawn (RL) (Table 1), referring to the methods in previous studies [34,35]. The above three land-use types are not only the primary land-use types of this main road, but also represent the major land-use types around the road network in the Leizhou Peninsula. The distance of each sampling site is about 0.6~1.4 km, avoiding roads, buildings, and water bodies. EPs in this area adopt standard operation modes, such as machine cultivation, ditching, land preparation, base fertilizer application, enhanced weeding, topdressing, etc., with a 3~4 year cutting cycle. ALs were distributed sporadically with Casuarina equisetifolia, Melia azedarach, Litsea glutinosa, and Delonix regia, as a secondary succession. RLs were covered by turfgrass, which is not allowed to be trampled, managed by the Bureau of Parks and Forestry, with unified fertilization, irrigation and mowing cycles.

2.3. Soil Sampling Collection Methods

At each sampling site, a 10 m × 10 m-plot was set and further divided into four connected subplots (5 m × 5 m each) for surficial soil sample collection. The average topsoil thickness of lateritic soil in the study area is 15~25 cm. Soil cores were obtained from two layers (0~10 cm and 10~20 cm depth) at each center of the subplot. The soil samples were placed in polyethylene bags and returned to the laboratory.

2.4. Laboratory Methods

Soil samples were air-dried, subjected to gentle grinding, sieved to 2.0 mm, and then kept at room temperature until subject to analysis. The particle size distribution was determined by the hydrometer method, dispersed in sodium hexametaphosphate [36], and used in soil texture classification, including sandy loam, clay loam, and silty loam. Bulk density (BD) was measured by metal cylinders of approximately 100 cm3 volume, with samples taken from the 0 to 10 cm and 10 to 20 cm soil layers, respectively, with six replicated samples taken from each plot. Natural water content (NWC) was determined by the oven drying method, with samples weighed and dried (105 °C) until they reached a constant weight. Total porosity (TPo) was calculated from BD: TPo (%) = (1–BD/PD) × 100%, where PD = particle density. Particle density was assumed to be 2.65 g cm−3.
Soil organic carbon (SOC) was measured by the K2Cr2O7-H2SO4 oxidation method [37]. Total nitrogen (TN) was determined by the microdiffusion method [38]. Total phosphorus (TP) was determined colorimetrically [39]. Total potassium (TK) was measured by flame emission spectroscopy [40]. Alkaline hydrolytic nitrogen (AN) was determined by the microdiffusion technique [38]. Available phosphorus (AP) was determined by spectrophotometry [39]. Available potassium (AK) was determined by flame photometry [40]. Exchangeable calcium (ECa) and magnesium (EMg) were determined by atomic absorption spectroscopy [41].

2.5. Statistical Analysis

Principal component analysis (PCA) was used to show the distribution of soil physicochemical properties, which was OC, TN, TP, TK, AN, AP, AK, ECa, EMg, BD, NWC, TPo, and texture, among different land uses. PCA was performed with the cross-product matrix of correlation and calculated scores for variables of the distance-based biplot, with a 999-run randomization test through PC-ORD version 6.0 [42].
Multi-response permutation procedures (MRPP) were performed to test the hypothesis that there was no difference between land uses in soil physicochemical property distribution using PC-ORD version 6.0 [42]. The result of MRPP provided a test statistic (T), an agreement statistic (A), and a p-value (p), where T describes the separation between the groups with more negative values of T, indicating stronger separation of the groups. The A statistic is given as a descriptor of within-group homogeneity, compared to the random expectation, with bigger A values indicating greater similarity within the groups [43]. The Sorensen (Bray–Curtis) distance was used as the MRPP distance measure to avoid the influence of outliers [43].
A non-parametric Kruskal–Wallis test alternative to one-way (between-groups) ANOVA was performed to evaluate the differences for every soil physicochemical property among the land uses. A non-parametric Kruskal–Wallis test was performed in Statistica 8.0.
Canonical correlation analysis was used to reflect the overall correlation between the physical property group and the chemical property group. Correlations between soil physicochemical properties were analyzed with Origin 2021.

3. Results

3.1. Effect of Land Use on Soil Physicochemical Property Pattern

PCA showed that the distribution of soil physicochemical properties revealed highly significant differences in the sampling site (p < 0.01) at both soil depths (Table 2). The soil physicochemical properties of EP and RL were obviously different, but the AL was similar to the other two land uses, to some degree (Figure 2). The first two principal component axes explained 64.21% of the total variation in the 0–10 cm soil layer, in which AK, ECa, EMg, NWC, BD, and TPo are the main indices determining soil differentiation, while it explained 69.41% of the total variation in the 10–20 cm soil layer, in which TN, TP AK, ECa, EMg, NWC, and BD are the main indices determining soil differentiation.
MRPP demonstrated that soil physicochemical property patterns exhibited extremely significant differences among the three land uses (p < 0.001) at both soil depths (Table 3). In pairwise comparison, soil physicochemical property patterns showed extremely significant difference between land uses (p < 0.001) at both soil depths, but no significant difference between AL and EP (p < 0.05) at a soil depth of 10–20 cm. The difference in the soil physicochemical property pattern between EP and RL was more significant than that between AL and EP, or AL and RL. The separation effect of land use on the soil physicochemical property pattern in the 0–10 cm soil layer was stronger than that in the 10–20 cm soil layer.

3.2. Difference in Soil Physicochemical Property among Land Uses

At the 0–10 and 10–20 cm depths, TN, TP, TK, AK, ECa, and EMg showed significant differences (p < 0.05) among the three land uses, and presented the descending order of RL > AL > EP (Figure 3b–d,g–i). AN showed only a marginal difference among the land uses at the 0–10 cm depth, but indicated a significant difference (p < 0.05) at the 10–20 cm depth (Figure 3e). OC and AP showed no significant differences at the 0–10 and 10–20 cm depths (Figure 3a,f). The TK, AK, ECa, and EMg of RL were much higher than those of the other two land uses. Compared with other land uses, each soil chemical property of EP was relatively concentrated.
At both soil depths, NWC exhibited a significant difference among the three land uses, and showed the descending order of RL > AL > EP (Figure 4b). BD indicated a significant difference among the land uses at the 0–10 cm depth, but no significant difference at the 10–20 cm depth (Figure 4a). TPo showed no significant difference among the three land uses (Figure 4c).

3.3. Correlation between Soil Physical and Chemical Properties

The correlation between physical and chemical properties was positive and highly significant, which at the 10–20 cm depth, was closer and more significant than that at the 0–10 cm depth (Figure 5). Moreover, the number of pairwise significant positive correlations of soil chemical properties at the 10–20 cm depth was higher than that at the 0–10 cm depth (Figure 6).
At both soil depths, NWC was extremely significantly negatively correlated with BD (p < 0.001), and extremely significantly positively correlated with TPo (p < 0.001) (Figure 6). BD was significantly negatively correlated with TPo (p < 0.001) at the 10–20 cm depth, but not significantly correlated with TPo (p < 0.05) at the 0–20 cm depth (Figure 6).
At the 0–10 cm depth, BD was significantly negatively correlated with TP, AK, ECa, and EMg (p < 0.05), and NWC was significantly positively correlated with TP, TK, AK, ECa, and EMg (p < 0.05) (Figure 6a). Meanwhile, TPo was significantly positively correlated with TK, ECa, and EMg (p < 0.05) in the upper layer (Figure 6a).
At the 10–20 cm depth, BD was significantly negatively correlated with TN, TP, AN, AK, ECa, and EMg (p < 0.05), and NWC was significantly positively correlated with TP, TK, ECa, and EMg (p < 0.05) (Figure 6b). Meanwhile, TPo was significantly positively correlated with TP, TK, and EMg (p < 0.05), but significantly negatively correlated with AP (p < 0.05) in the lower layer (Figure 6b).

4. Discussion

The distribution of soil physicochemical properties was affected by many factors, and some played a leading role in certain situations. In the tropical coastal ecologically fragile zone, land use was important in determining the significant differentiation of topsoil physicochemical properties. Land use practice, including planting, fertilizing, weeding, harvesting, etc., influenced soil moisture and nutrients related to soil processes, such as erosion, oxidation, mineralization, and leaching [2,9,44].
No significant differences were noted regarding soil organic carbon, available phosphorus, and porosity among different land uses. The recovery time of soil organic carbon was slow [45], which can effectively improve soil porosity [46]. Available phosphorus, which was held firmly in crystal lattices of largely insoluble forms [47], was determined primarily by the parent rock and not easily subject to land use change. Soil total nitrogen, available nitrogen, total phosphorus, total potassium, available potassium, exchangeable calcium, exchangeable magnesium, and nature water content all exhibited distinct differences among land-use types in both soil layers. Land use had been shown to influence the soil physicochemical properties due to erosion, compaction, and porosity development [34,35,48].
Marked distinctions in soil physicochemical parameters existed between the eucalyptus plantation and road lawn land types. The road lawn had higher soil nutrients, soil water content, total porosity, and the lower soil bulk density than the eucalyptus plantation. This differed from the results of previous studies, which showed that shrubs and trees supported much higher soil nutrient levels than did native grassland in the same climate region [3,44]. Unlike natural ecosystems, the input of soil organic matter in artificial ecosystems can also come from fertilization, in addition to the decomposition of plant litter [49,50]. Grass typically features shallow rooting depth, low organic matter accumulation, and is generally associated with lower faunal activity than forests or shrubs [14]. Soil processes in artificial ecosystems, such as eucalyptus plantations and road lawns, were quite different from those in natural ecosystems. The reasons were as follows:
Eucalyptus planting in Leizhou Peninsula has a long history of more than 60 years and a wide adoption of standard operation modes [21,26]. This was also the reason for the convergence of soil physicochemical properties in the eucalyptus plantation. The eucalyptus plantation demonstrated that nutrient consumption was higher than what was supplied through just base fertilizer application [28]. The period of clear-cutting of eucalyptus plantations in the Leizhou Peninsula had shortened to 3~4 years [27]. In the harvest of eucalyptus plantations, all the logs, as well as the twigs and leaves, were cleared up from the land, leading to no residual litter or natural nutrient input. All of the above cause soil nutrient loss, soil compaction, reduced porosity, and increased water consumption in eucalyptus plantations, such land overuse exacerbates land degeneration in the Leizhou Peninsula [50].
Abandoned land involved no intervention or treatment of litter. Litter decomposition led to soil nutrient inputs [51,52], which also could reduce soil compaction, maintain soil moisture, and improve pore structure, mainly by buffering human trampling and reducing surface runoff [53]. However, the spontaneous recovery time of soil nutrients was slow [43], especially in vulnerable ecosystems where soil erosion was serious, such as those of the laterite type in the Leizhou Peninsula.
Road lawn maintenance requires regular fertilization and irrigation, which has drastically altered soil nutrients and water budgets in the road lawn, often to a degree that masks natural variability [17]. Litter from lawn mowing also becomes another source of soil nutrient input [54]. Fertilization, mowing, and irrigation make the soil nutrient and water of road lawn higher than other those of other land uses in this study. Although the road lawn is managed uniformly, the land use history is different, so the soil physicochemical properties were scattered. In the road lawn, ground vegetation cover is almost complete, and the root depth is mainly concentrated in the surficial soil. Plus, no trampling is allowed in the road lawn. Together, both can create a low bulk density and a high porosity structure of the topsoil in the road lawn. The above artificial interventions improve the topsoil quality in ecologically fragile areas.
The physical properties of soil are closely and significantly related to the chemical properties in this study. Previous studies also found that soil porosity and structural characteristics were key factors affecting variations in soil organic carbon and soil nutrients [48]. Correlations between physical and chemical properties in the lower layer are closer than in the upper layer because the relationship of the surficial physicochemical properties is more susceptible to change of land use and vegetation pattern [8,34,55].

5. Conclusions

Soil nutrients (total nitrogen, available nitrogen, total phosphorus, total potassium, available potassium, exchangeable calcium, exchangeable magnesium) and water significantly decrease from road lawn to abandoned land to eucalyptus plantation land types. Overuse of land (eucalyptus plantation) without a rest in the ecologically fragile zone leads to soil erosion and compaction. Compared with natural restoration (abandoned land), artificial restoration (road lawn), accompanied by watering, fertilization, litter returning, and increasing vegetation coverage, can improve soil nutrient and water status in tropical coastal ecologically fragile zones more quickly. Land use can interfere with soil nutrient and water input, and change the soil structural characteristics, but cannot modify the soil organic carbon and porosity in the short term. It takes a long time to recover soil organic carbon and improve soil porosity to enable their use in measuring ecological restoration. Understanding the effects of land use on soil physicochemical properties is crucial to control soil and water loss in ecologically fragile zones. Since the chemical properties of soil are intimately connected to the physical properties, to improve the soil quality in tropical coastal ecologically fragile zones, physical and chemical properties are equally important. It is necessary to further study the temporal-spatial variations of soil structure and nutrients under different land uses, which is conducive to soil and water conservation in ecologically fragile areas. Combined with soil leachate, deep soil can also be further considered.

Author Contributions

Conceptualization, Y.O., G.Q.-B., and C.W.; methodology, Y.O.; software, Y.O. and G.Q.-B.; validation, Y.O., G.Q.-B., and C.W.; formal analysis, Y.O.; investigation, Y.O. and C.W.; resources, Y.O. and C.W.; data curation, Y.O.; writing—original draft preparation, Y.O.; writing—review and editing, Y.O., G.Q.-B., and C.W.; visualization, Y.O. and G.Q.-B.; supervision, Y.O.; project administration, Y.O. and C.W.; funding acquisition, Y.O. and C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Forestry Technology Innovation Project of Guangdong Province, grant number 2018KJCX027.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All relevant data are included in the paper.

Acknowledgments

We thank C. Rhett Jackson, from the Warnell School of Forestry and Natural Resources, University of Georgia, who provided constructive comments on an earlier draft of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area and sampling sites in the Mazhang District of the Leizhou Peninsula, China.
Figure 1. Study area and sampling sites in the Mazhang District of the Leizhou Peninsula, China.
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Figure 2. Biplot of the first two components from the principal component analysis (PCA) under different land uses. OC: organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: available nitrogen; AP: available phosphorus; AK: available potassium; ECa: exchangeable calcium; EMg: exchangeable magnesium; BD: bulk density; NWC: nature water content; TPo: total porosity.
Figure 2. Biplot of the first two components from the principal component analysis (PCA) under different land uses. OC: organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: available nitrogen; AP: available phosphorus; AK: available potassium; ECa: exchangeable calcium; EMg: exchangeable magnesium; BD: bulk density; NWC: nature water content; TPo: total porosity.
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Figure 3. Multiple box-plots of the topsoil chemical properties in two layers (0–10 cm and 10–20 cm depth) among three land-use types.
Figure 3. Multiple box-plots of the topsoil chemical properties in two layers (0–10 cm and 10–20 cm depth) among three land-use types.
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Figure 4. Multiple means with error plots of topsoil physical properties in two layers (0~10 cm and 10~20 cm depth) among three land-use types.
Figure 4. Multiple means with error plots of topsoil physical properties in two layers (0~10 cm and 10~20 cm depth) among three land-use types.
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Figure 5. Scatter plot of the canonical correlation analysis (CCA) between the left set and right set. Left set includes 9 variables, which are organic carbon, total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, exchangeable calcium, and exchangeable magnesium. Right set includes 3 variables, which are bulk density, natural water content, and total porosity.
Figure 5. Scatter plot of the canonical correlation analysis (CCA) between the left set and right set. Left set includes 9 variables, which are organic carbon, total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, exchangeable calcium, and exchangeable magnesium. Right set includes 3 variables, which are bulk density, natural water content, and total porosity.
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Figure 6. Correlation analysis between soil physicochemical properties (* p < 0.05; ** p < 0.01; *** p < 0.001). OC: organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: available nitrogen; AP: available phosphorus; AK: available phosphorus; ECa: exchangeable calcium; EMg: exchangeable magnesium; BD: bulk density; NWC: natural water content; TPo: total porosity. The numbers represent the correlation coefficients. Red and blue denote positive and negative correlations, respectively.
Figure 6. Correlation analysis between soil physicochemical properties (* p < 0.05; ** p < 0.01; *** p < 0.001). OC: organic carbon; TN: total nitrogen; TP: total phosphorus; TK: total potassium; AN: available nitrogen; AP: available phosphorus; AK: available phosphorus; ECa: exchangeable calcium; EMg: exchangeable magnesium; BD: bulk density; NWC: natural water content; TPo: total porosity. The numbers represent the correlation coefficients. Red and blue denote positive and negative correlations, respectively.
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Table
Table 1. Site characteristics: land use, distance from the coast, soil texture, topography, elevation, and slope of soil sampling sites. Site numbers correspond to Figure 1.
Table 1. Site characteristics: land use, distance from the coast, soil texture, topography, elevation, and slope of soil sampling sites. Site numbers correspond to Figure 1.
SiteSoil TypeLand UseMSL mSlope %
1LithosolsAbandoned land (AL)138.75
2Abandoned land (AL)153.32
3Abandoned land (AL)151.75
4Eucalyptus plantation (EP)104.89
5Road lawn (RL)171.75
6Abandoned land (AL)157.87
7Road lawn (RL)163.49
8Road lawn (RL)2012.49
9LuvisolsAbandoned land (AL)2016.40
10Road lawn (RL)2711.75
11Road lawn (RL)2418.75
12Eucalyptus plantation (EP)2117.63
13Eucalyptus plantation (EP)2614.05
14Eucalyptus plantation (EP)167.63
15Eucalyptus plantation (EP)2312.28
16Eucalyptus plantation (EP)196.12
17Eucalyptus plantation (EP)196.12
18Eucalyptus plantation (EP)2015.24
19Abandoned land (AL)2013.67
20Road lawn (RL)2912.62
21Abandoned land (AL)2813.49
22Road lawn (RL)2818.75
23Eucalyptus plantation (EP)2651.24
24Abandoned land (AL)2411.75
Note: MSL-mean sea level.The sea level was measured in each subplot and then averaged.
Table
Table 2. PCA of the soil physicochemical properties.
Table 2. PCA of the soil physicochemical properties.
0–10 cm10–20 cm
PC 1PC 2PC 1PC 2
Eigenvalue5.472.886.302.72
% of Variance42.0922.1248.4620.95
P0.0010.0010.0010.001
VariableEigenvector
OC−0.451−0.860−0.5780.633
TN−0.559−0.790−0.7310.394
TP−0.4340.319−0.869−0.131
TK−0.6390.257−0.594−0.037
AN−0.458−0.827−0.6580.598
AP−0.087−0.0550.3300.432
AK−0.7840.031−0.7770.464
ECa−0.9010.018−0.7830.317
EMg−0.8090.146−0.7890.024
BD0.813−0.0800.7730.155
NWC−0.9060.273−0.787−0.588
TPo−0.6950.338−0.657−0.692
Texture−0.3090.663−0.542−0.664
Table
Table 3. Pairwise comparisons of soil nutrients within the land-use type and the soil texture class in MRPP.
Table 3. Pairwise comparisons of soil nutrients within the land-use type and the soil texture class in MRPP.
Environmental Variable0–10 cm10–20 cm
TAPTAP
Land Use−10.390.50<0.001−8.890.45<0.001
Groupvs.GroupPairwise comparisons
ALvs.EP−7.140.23<0.001−1.340.050.099
ALvs.RL−7.120.39<0.001−6.580.39<0.001
EPvs.RL−9.000.56<0.001−7.950.48<0.001
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Ou, Y.; Quiñónez-Barraza, G.; Wang, C. Effects of Land-Use Types on Topsoil Physicochemical Properties in a Tropical Coastal Ecologically Fragile Zone of South China. Sustainability 2023, 15, 5484. https://doi.org/10.3390/su15065484

AMA Style

Ou Y, Quiñónez-Barraza G, Wang C. Effects of Land-Use Types on Topsoil Physicochemical Properties in a Tropical Coastal Ecologically Fragile Zone of South China. Sustainability. 2023; 15(6):5484. https://doi.org/10.3390/su15065484

Chicago/Turabian Style

Ou, Yuduan, Gerónimo Quiñónez-Barraza, and Chubiao Wang. 2023. "Effects of Land-Use Types on Topsoil Physicochemical Properties in a Tropical Coastal Ecologically Fragile Zone of South China" Sustainability 15, no. 6: 5484. https://doi.org/10.3390/su15065484

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