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Article

Effects of Tree Species and Soil Enzyme Activities on Soil Nutrients in Dryland Plantations

by
Yage Li
1,2,
Chun Han
1,2,
Shan Sun
1,2 and
Changming Zhao
1,2,*
1
State Key Laboratory of Grassland Agro-Ecosystems, School of Life Sciences, Lanzhou University, Lanzhou 730000, China
2
Gansu Provincial Field Scientific Observation and Research Station of Mountain Ecosystems, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Forests 2021, 12(9), 1153; https://0-doi-org.brum.beds.ac.uk/10.3390/f12091153
Submission received: 9 July 2021 / Revised: 20 August 2021 / Accepted: 23 August 2021 / Published: 26 August 2021
(This article belongs to the Section Forest Soil)
Browse Figures
Versions Notes

Abstract

:
Long-term afforestation strongly changes the soil’s physicochemical and biological properties. However, the underlying mechanism of different tree species driving change in soil nutrients is still unclear in the long-term dryland plantations of the Loess Plateau, China. In this study, samples of surface soil (0–20 cm) and woody litter were collected from five plantations (≥50 years) of Caragana korshinskii, Armeniaca sibirica, Populus hopeiensis, Platycladus orientalis, and Pinus tabulaeformis and a natural grassland, and tested for the carbon, nitrogen, phosphorus, and potassium contents, as well as the soil sucrase (SC), urease (UE), and alkaline phosphorus (ALP) activities. We found that soil nutrients, enzyme activities, and the litter’s chemical properties obviously varied among five tree species. C. korshinskii significantly increased the soil’s TC, organic carbon (OC), total nitrogen (TN), available nitrogen (AN), and available potassium (AK) by 28.42%, 56.08%, 57.41%, 107.25%, and 10.29%, respectively, and also increased the soil’s available phosphorus (AP) by 18.56%; while P. orientalis significantly decreased soil TN (38.89%), TP (30.58%), AP (76.39%), TK (8.25%), and AK (8.33%), and also decreased soil OC (18.01%) and AN (1.09%), compared with those in grassland. The C. korshinskii plantation had higher quality litter and soil enzyme activities than the P. orientalis plantation. Moreover, 62.2% of the total variation in soil nutrients was explained by the litter’s chemical properties and soil enzyme activities, and the litter phosphorus (LP) and soil ALP had a more significant and positive impact on soil nutrients. Therefore, tree species, LP, and soil ALP were key factors driving soil nutrient succession in dryland plantations. The significantly positive nitrogen–phosphorus coupling relationship in the “litter–enzyme–soil” system revealed that the improving nitrogen level promoted the phosphorus cycle of the plantation ecosystem. Our results suggest that leguminous tree species are more suitable for dryland afforestation through the regulation of litter quality and soil enzyme activities.

1. Introduction

The artificial forest area in the world has reached 2.78 × 106 km2 in 2015 [1], and afforestation is considered an effective measure to alleviate soil erosion and land degradation [2,3]. The conversion from non-forestlands to artificial forestlands has significantly changed aboveground vegetation and the biological community and physicochemical properties of the soil [4,5]. The temporal dynamics of and differences in soil nutrients in plantations of different tree species along afforestation chronosequences have been extensively studied [6,7]. After afforestation, plantations promote the accumulation of litter while stimulating soil biological activities and producing more enzymes [8,9]. However, the effects of litter as a supplementary source of soil nutrients and enzymes as indicators of soil fertility on the succession of soil nutrients in plantations have rarely been considered. Therefore, whether and how the litter of different tree species and soil enzyme activities drive soil nutrients in plantations remains to be studied.
The effects of different tree species plantations on soil nutrient contents are quite different on a global scale; for example, broadleaf tree species promote an increase in soil available phosphorus (7–18%), while coniferous tree species cause uncertain changes in soil available phosphorus (−3–16%) [3]. Evergreen plantations of Pinus oocarpa and Eucalyptus camaldulensis led to reduced or stable changes in surface soil organic matter, total nitrogen, and exchangeable nutrients [10]. The impact of these different tree species on soil nutrients may be regulated by litter quantity and quality because litter plays an important role in soil nutrient cycling [7,11]. Large numbers of litter addition experiments have proven that the presence of litter had a positive effect on soil nutrients [12], and litter mixing experiments of different species showed that the chemical properties of litter were closely related to soil nutrients [13]. Therefore, the litter is the key to nutrient cycling in forest ecosystems [14]. The nitrogen and nitrogen–phosphorus ratio in litter significantly affected soil nutrient properties by controlling the litter decomposition rate [7]. The influence of litter quality of different species on soil phosphorus availability depended on the phosphorus content of the litter, and phosphorus-rich leaf litter could release more phosphorus into soil during the decomposition process, which would improve soil phosphorus availability [12]. Thus, in the process of ecological restoration of plantations, it is of great significance to clarify the effects of the chemical properties of litter from different tree species on soil nutrient contents for the selection of afforestation tree species and the management of plantations.
Enzymes participate in the litter decomposition process and are involved in the chemical element cycle of the ecosystem [15], for example, sucrase, urease, and phosphatase, respectively, hydrolyze sucrose, urea, and phosphate monoester to produce glucose and fructose, ammonia and carbon dioxide or ammonium carbonate, and phosphate anion [16]. Many studies have reported that soil enzyme activities were regulated by temperature, moisture, pH, plant biomass, tree species, and microbial communities, which, in turn, affected soil nutrients [17,18]. Soil enzyme activities were significantly correlated with soil nutrients and had the potential to indicate soil quality and nutrient balance [5,15,19]. The soil’s soluble organic carbon, soluble organic nitrogen, and available phosphorus increased significantly with the increase in soil catalase, saccharase, urease, and alkaline phosphatase activities in legume plantations [20]. The stoichiometry of soil carbon, nitrogen, and phosphorus-acquiring enzyme activities can reveal the limiting elements affecting the growth of plants and microbes [15]. In natural Quercus wutaishansea forests, the lower β-1,4-glucosidate to β-1,4-N-acetylglucosaminidate ratio and the β-1,4-glucosidate to alkaline phosphatase ratio represented greater nitrogen and phosphorus demand, respectively [21]. However, in long-term dryland plantations, how soil enzyme activities respond to the effects of different tree species on soil nutrients is unclear due to differences in litter specificity and site conditions. Therefore, understanding the relationships among soil enzyme activities, litter properties, and soil nutrients is the key to exploring the changing mechanism of soil fertility in dryland plantations.
The total area of the Loess Plateau in China is about 6.28 × 105 km2, spanning semi-humid, semi-arid, and arid areas, and the natural vegetation is severely fragmented, with only a small part of natural grassland and forest, and some bare land; most of the other areas are farmland [22]. The expansion of farmland and the reduction of vegetation have caused serious soil erosion problems [22]. Since 1950, large-scale afforestation activities have been carried out in the Loess Plateau, especially the “Grain for Green project” in 1999 [2]. Caragana korshinskii, Armeniaca sibirica, Populus hopeiensis, Platycladus orientalis, Pinus tabulaeformis, etc. are common afforestation tree species in the Loess Plateau because of their nitrogen fixation, drought resistance, and/or rapid growth characteristics [22]. Many studies have focused on the changes in litter decomposition, soil enzyme activities, and nutrient properties in plantations in semi-humid areas [19,23,24]. However, in dryland plantations, the effects of different tree species on soil nutrients may be small and slow, and the soil nutrients’ response to litter properties and soil enzymes may be different compared with that in other regions. Therefore, the objective of this study was to (1) compare the changes in the soil nutrients of different tree species plantations after long-term afforestation, (2) analyze the differences in the chemical litter properties and soil enzyme activities of different tree species plantations, and (3) reveal the relationship among the chemical litter properties, soil enzyme activities, and soil nutrients in dryland plantations. We hypothesized that long-term afforestation would promote the increase in soil nutrients and enzyme activities, but may be reduced by the influence of evergreen tree species. Due to the phosphorus limitation in the Loess Plateau [25], we predicted that the litter phosphorus content and alkaline phosphatase were important factors that would determine the change in soil nutrients in dryland plantations.

2. Materials and Methods

2.1. Study Area

The study site is located at the Gongjing Forest Farm (35.9° N, 104.3° E) in Yuzhong County, Gansu Province, western Loess Plateau, China. The Gongjing Forest Farm was established in 1959, with about 86 km2 and an average altitude of 2250 m a.s.l. The forest farm is located in a semi-arid area and has a temperate continental monsoon climate. The sum of annual precipitations is 381 mm, the annual average temperature is 7.2 °C (1985–2019), and more than 50% of the annual precipitation is concentrated from May to September. Over the past 35 years, the annual precipitation and temperature have shown an increasing trend. The soil type is loessial soil (Calcaric Cambisol, FAO classification), with no gravel. The five plantations are pure forests of C. korshinskii, A. sibirica, P. hopeiensis, P. orientalis, and P. tabulaeformis. Before afforestation, the land use in this study area was cropland planted to maize (Zea mays) and foxtail millet (Setaria italica).

2.2. Experimental Design

Under similar soil and climatic conditions, analyzing the changes in and interactions of plants and soil is a widely used method in ecological research [10,26]. In this study, we selected C. korshinskii (55 years), A. sibirica (50 years), P. hopeiensis (50 years), P. orientalis (50 years), and P. tabulaeformis (50 years) plantations as study plots and a natural grassland near the plantations as a reference plot. The distance between the different plantations and the grassland is less than 10 km, and their abiotic factors are consistent, so it was considered that the plants were the key factors affecting soil nutrient cycling [27]. The dominant species of the grassland were Artemisia ordosica and Festuca ovina, and the community height was about 10 cm. Detailed information of the plantations and grassland is shown in Table 1.
A field investigation and sampling were conducted in early July and October 2019. There were five 20 m × 20 m squares in each plantation and five 1 m × 1 m squares in a natural grassland, which were randomly chosen, with more than 30 m of distance between two squares in each plot. Vegetation had the greatest impact on the surface (0–20 cm) soil [28], so we collected the surface (0–20 cm) soil. In each square, soil and litter samples were collected according to the diagonal method (5 points) and mixed into a soil sample and a litter sample, respectively.

2.3. Litter and Soil Sampling

Before collecting the soil samples, we first removed the litter, herbs, and crusts, then used a 5 cm soil drill to obtain the top 0–20 cm of the soil. After thoroughly mixing the soil at 5 points, we removed the plant residues and divided them equally into 3 parts. The first soil sample was packed in an aluminum box to determine the soil water content. The second soil sample was air-dried in a natural environment without light and passed through a 2 mm sieve to determine the chemical properties. The third soil sample was stored at 4 °C for determination of the soil enzyme activities. Simultaneously, at the center of each square sample, a steel cylinder (ring knife) with a volume of about 100 cm3 was used to collect a portion of the soil that remained intact to determine the soil bulk density. In total, 120 soil samples were obtained: 30 squares × 4 parts. In addition, the leaf litter of woody plants was collected from each plantation square, dried, and crushed, and a total of 25 litter samples were obtained.

2.4. Measurement of Litter and Soil Properties

Using the combustion method, we determined the litter carbon (LC, g·kg−1) and nitrogen (LN, g·kg−1) and soil total carbon (TC, g·kg−1) and nitrogen (TN, g·kg−1), tested by the Elementar vario MACRO cube Organic Element Analysis (Germany Elementar) [14]. Litter phosphorus (LP, g·kg−1) and soil total phosphorus (TP, g·kg−1) were measured using the molybdenum antimony colorimetric method after the litter was digested with the concentrated sulfuric acid and hydrogen peroxide, and the soil was digested with concentrated sulfuric acid and perchloric acid [29]. The LP and TP were tested by the Smartchem 140 automatic discontinuous chemical analyzer (France Alliance). Litter potassium (LK, g·kg−1) and soil total potassium (TK, g·kg−1) were measured using the flame atomization method after digestion [30], and tested by the TRACE AI1200 atomic absorption spectrometer (Canada Aurora).
Using the potassium dichromate external heating method, we determined the soil organic carbon (OC, g·kg−1). After the soil was leached by potassium chloride, using the indophenol blue colorimetric method and the phenol disulfonic acid colorimetric method, we determined the soil ammonium nitrogen and nitrate nitrogen. Soil available nitrogen (AN, mg·kg−1) was defined as the sum of soil ammonium nitrogen and nitrate nitrogen. Soil available phosphorus (AP, mg·kg−1) was measured using the molybdenum antimony colorimetric method after sodium bicarbonate leaching [29]. The AN and AP were tested by the Smartchem 140 automatic discontinuous chemical analyzer. Soil available potassium (AK, mg·kg−1) was measured using the flame atomization method after ammonium acetate leaching [30], and tested by the TRACE AI1200 atomic absorption spectrometer.
The soil samples in the steel cylinder and aluminum box were dried and weighed to determine the soil bulk density (BD, g·cm−1) and soil water content (SWC, %). BD was equal to the ratio of the dry soil mass to 100 cm3, and SWC was equal to 100 × the ratio of the soil water mass to the dry soil mass [15,24]. A pH meter was used to determine the soil pH in a 1:5 w/v soil–water filtration solution [24].

2.5. Measurement of Soil Enzyme Activities

The soil enzyme activities were measured using fresh soil samples stored at low temperatures and converted into dry soil enzyme activities through SWC. The 3,5-dinitrosalicylic acid colorimetry method, indophenol blue colorimetry method, and phenol colorimetric method were used by a spectrophotometer to determine soil sucrase (SC, mg·d−1·g−1), urease (UE, mg·d−1·g−1), and alkaline phosphatase (ALP, mg·d−1·g−1), respectively [16].

2.6. Statistical Analysis

A one-way ANOVA and a least significant difference (LSD) multiple comparison test (p < 0.05) were used to compare the differences in the soil physicochemical properties, enzyme activities, and chemical litter properties of different tree species plantations. A redundancy analysis (RDA) was used to estimate the contribution of the effect variables (chemical litter properties and soil enzyme activities) on response variables (soil nutrients). A bivariate correlation analysis was used to explore the relationships among chemical litter properties, soil enzyme activities, and soil nutrients. In this study, SPSS 17.0, OriginPro 2021, and CANOCO 4.5 were used for data analysis and charting.

3. Results

3.1. Soil Physicochemical Properties

The soil BD, pH, and SWC were significantly different between the five plantations and the grassland (Table 2). Compared with grassland, plantations reduced soil BD and SWC by 3.36–17.65% and 3.81–61.7%, and soil pH varied from 8.05 in grassland to 7.87 in P. tabuliformis plantations. The soil BD of P. orientalis, A. sibirica, and P. tabuliformis was significantly greater than that of C. korshinskii and P. hopeiensis (p < 0.05). The highest SWC (13.89%) was observed in the P. hopeiensis plantation, which was higher than that of C. korshinskii, A. sibirica, P. orientalis, and P. tabuliformis by 14.54%, 56.52%, 60.19%, and 37.01%, respectively. The lowest SWC (5.53%) was observed in the P. orientalis plantation, which was significantly lower than that (11.87%) of C. korshinskii plantation by about 50% (p < 0.05). The soil pH of C. korshinskii, P. hopeiensis, and P. orientalis was significantly greater than that of P. tabuliformis (7.87).
The effects of different tree species plantations on soil carbon, nitrogen, phosphorus, and potassium are shown in Figure 1. The soil TP and TK in plantations were significantly lower than those in grassland by 11.87–30.58% and 4.69–8.25% (p < 0.05), but the soil TC in plantations was higher than that in grassland by 1.69–28.42%. The soil TC, TN, OC, AN, and AK of the C. korshinskii plantation were 37.96 g·kg−1, 1.70 g·kg−1, 15.96 g·kg−1, 15.12 mg·kg−1, and 71.77 mg·kg−1, respectively, and significantly higher than those in grassland by 28.42%, 57.41%, 56.08%, 107.25%, and 10.29% (p < 0.05); its soil AP also increased by 19%. However, the soil TN, TP, AP, TK, and AK of the P. orientalis plantation decreased significantly by 38.89%, 30.58%, 7.64%, 8.25%, and 8.33% relative to those in grassland. Except for the C. korshinskii and P. orientalis plantations, the soil AP of grassland (2.41 mg·kg−1) was significantly higher than that of A. sibirica, P. hopeiensis, and P. tabuliformis plantations by 82.26%, 162.15%, and 61.04%, respectively (p < 0.05). There was no significant difference in soil OC and AN between grassland, A. sibirica, P. hopeiensis, and P. tabuliformis plantations, and no significant difference in soil TC, TN, and AK between grassland, and A. sibirica, and P. tabuliformis plantations (p > 0.05).

3.2. Chemical Litter Properties

The chemical litter properties of the five tree species plantations were significantly different, as shown in Figure 2. The LC of different tree species plantations was as follows: P. tabuliformis (519.00 g·kg−1) > P. orientalis (502.32 g·kg−1) > C. korshinskii (455.06 g·kg−1) > A. sibirica (417.06 g·kg−1) > P. hopeiensis (414.40 g·kg−1) (p < 0.05). C. korshinskii, a leguminous species, had significantly higher LN than that of other four tree species by 59.25–386.62% (p < 0.05), and the LN of P. orientalis was the lowest (5.38 g·kg−1). The LP of different tree species plantations was as follows: C. korshinskii (1.32 g·kg−1) > P. hopeiensis (0.50 g·kg−1) > P. tabuliformis (0.33 g·kg−1) ≈ A. sibirica (0.32 g·kg−1) > P. orientalis (0.17 g·kg−1); the LP in C. korshinskii was significantly higher than that in other four tree species by 161.72–310.98% (p < 0.05). The LK (12.58 g·kg−1) of the A. sibirica plantation was significantly higher than that of C. korshinskii, P. hopeiensis, P. orientalis, and P. tabuliformis by 158.16–515.84% (p < 0.05), and the LK of the P. tabuliformis plantation was the lowest (2.04 g·kg−1). The changing trends of LN and LP in different tree species plantations were similar.

3.3. Soil Enzyme Activities

There was no significant difference in soil SC between the five plantations and grassland, but soil UE and ALP showed significant changes (Figure 3). The soil SC of C. korshinskii (90.93 mg·d−1·g−1) was higher than that of grassland (79.99 mg·d−1·g−1), A. sibirica (87.46 mg·d−1·g−1), P. hopeiensis (80.39 mg·d−1·g−1), P. tabuliformis (80.59 mg·d−1·g−1), and P. orientalis (77.78 mg·d−1·g−1) by 3.97–16.91% (p > 0.05). The highest soil UE (0.94 mg·d−1·g−1) and ALP (2.75 mg·d−1·g−1) were observed in the C. korshinskii plantation, which were significantly higher than those of grassland and the other four tree species plantations by 13.96–36.99% (UE) and 42.56–178.25% (ALP) (p < 0.05). P. orientalis also had the lowest soil SC (77.78 mg·d−1·g−1), UE (0.69 mg·d−1·g−1), and ALP (0.99 mg·d−1·g−1).

3.4. Relationships among Chemical Litter Properties, Soil Enzyme Activities, and Soil Nutrients

RDA showed that the chemical litter properties, soil enzyme activities, and the nutrients of different tree species plantations were quite different (Figure 4). The five plantations were clustered into four groups, and C. korshinskii, P. hopeiensis, and A. sibirica were all grouped into one group, while P. orientalis and P. tabuliformis were clustered into one group, with similar soil and litter properties. The 62.2% of soil nutrient variation could be explained by the chemical litter properties and soil enzymes, for which the contribution rate of the first and second axes was 56.5%. The soil SC, UE, and ALP positively affected soil TK, AK, TP, AP, TN, AN, and OC; the three soil enzyme activities were positively related with each other. In addition to soil TK, the LC negatively affected soil nutrients, and the LK had less positive or negative effects on soil nutrients. However, the LN and LP positively affected the soil TC, OC, TN, AN, TP, AP, and AK, and a positive relationship was observed between LN and LP. The positive influences of the LN, LP, and ALP on soil nutrient variability reached a significant level (p < 0.05).
Bivariate correlation analysis illustrated the correlations among chemical litter properties, soil enzyme activities, and nutrients (Table 3). Significant correlation coefficients existed between LC and LN (−0.56), LC and LK (−0.51), and LN and LP (0.96) (p < 0.01). The soil UE was significantly positively correlated with the soil SC and ALP (p < 0.05 and 0.01, respectively). There was a significant negative correlation between LC and soil UE (p < 0.05). However, the LN and LP were extremely significantly positively correlated with soil UE and ALP, and the correlation coefficient range was 0.88–0.96 (p < 0.01). The LC was significantly negatively correlated with soil TN and AK, but the LN and LP were significantly positively correlated with soil TC, OC, TN, AN, AP, and AK (p < 0.05). In addition to soil TC, the correlation coefficients between LP and soil nutrients were larger than those between LN and soil nutrients. The LK only had a significant positive correlation with soil AK (p < 0.05). The three soil enzyme activities were significantly positively correlated with soil OC, TN, AP, and AK (p < 0.05 or 0.01), and the soil UE and ALP were extremely significantly positively correlated with soil AN (p < 0.01). Compared with soil UE and SC, soil ALP had a larger correlation coefficient with soil TC, TN, AP, and AK. The chemical litter properties and soil enzyme activities had no significant correlation with soil TP and TK (p >0.05).

4. Discussion

4.1. Effects of Tree Species on Soil Nutrients

Long-term afforestation with different tree species significantly changed soil BD, pH, and SWC. The decrease in soil BD was caused by the more intense and frequent activities of more abundant soil animals and microorganisms after afforestation [31]. Except for P. hopeiensis, the other four tree species plantations significantly decreased SWC (Table 2), which was consistent with the findings of Cao et al. [30], who reported that large-scale afforestation resulted in soil drying and water depletion. Compared with grassland, woody plants consumed more soil water, and the canopy and litter layers of plantations intercepted precipitation [32]. We also found that the pH of alkaline soils decreased after long-term afforestation, because woody plants could produce more organic acids or anions that entered the soil [33]. A meta-analysis showed that afforestation promoted soil pH neutralization [34]. In this study, the SWC and pH of broadleaf tree species (C. korshinskii and P. hopeiensis) were higher than those of coniferous tree species (P. orientalis and P. tabuliformis) by 35.66–151.18% and 0.50–2.29%, suggesting that the impact of tree species on soil properties was species-specific [34], which may be due to differences in water requirements, plant traits, litter decomposition, root exudate, and soil ion exchange [33,35,36]. Thus, to a certain extent, the degree of changes in soil physicochemical properties after afforestation depended on different tree species.
Tree species significantly affected soil nutrients in dryland plantations, and decided the direction and degree of soil nutrient succession. Long-term afforestation increased soil TC and decreased soil TP and TK because the afforestation drove the accumulation of more tree litter and faster soil mineralization to meet tree growth [3,37,38]. Significantly different soil nutrients were found in the five plantations, and C. korshinskii improved soil nutrients, but P. orientalis and P. tabuliformis, as evergreen tree species, reduced soil nutrients, which supported our hypothesis about the different effects of tree species on soil nutrients. The findings of Wang et al. [39] were consistent with ours, as they noted that the impacts of 13 tree species on soil fertility had an interspecific difference, and P. tabuliformis had the lowest soil fertility. Because deciduous and evergreen tree species had significantly different litter [40,41], the litter mixing test of deciduous and evergreen trees revealed that the high litter carbon and lignin contents of evergreen trees led to a low decomposition rate and slow nutrient release [40,41]. Our results also showed that deciduous plantations (C. korshinskii and P. hopeiensis) had higher soil TC, OC, TN, AN, and AK contents than evergreen plantations (P. orientalis and P. tabuliformis) by 1.34–166.87% (Figure 1). In addition, C. korshinskii was not only a deciduous tree species but also a leguminous tree species. The leguminous tree species had higher nitrogen and carbon input from fixation [42]. Gei and Powers [6] compared the effects of legumes and non-leguminous tree species on soil properties in Costa Rican dry plantations, and confirmed that legumes had higher soil TC, TN, and nitrate nitrogen contents. These findings indicated that deciduous tree species, especially legumes, played a more important role than evergreen tree species in dryland vegetation restoration [15,23], which produced high-quality and easily decomposable litter, promoting nutrient cycling and accumulation [43]. Therefore, the differences in soil nutrients in different plantations may be caused by the chemical litter properties of different tree species.

4.2. Effects of Tree Species on Chemical Litter Properties

The chemical litter properties were significantly affected by different tree species, due to the interspecific differences [44]. The significant difference in litter quality among different tree species has been reported in previous studies [40,41]. Xu et al. [45] illustrated that Robinia pseudoacacia, C. korshinskii, A. sibirica, and P. tabuliformis had significant differences in nitrogen and phosphorus resorption efficiency, green leaf nutrients, and growth rates, which may have led to the production of different quality litters [46]. In our study, high-quality litters (higher LN, LP, and LK with lower LC) were observed in C. korshinskii, A. sibirica and P. hopeiensis, while low-quality litters (higher LC with lower LN, LP, and LK) were found in P. orientalis and P. tabuliformis. We also found that the LC, LN, and LP under P. tabuliformis were significantly higher than those under P. orientalis by 3.32%, 34.20%, and 96.65%, respectively. However, Bai et al. [46] noted that P. tabuliformis had lower LN and LP than P. orientalis, which may be caused by abiotic factors, for example, different temperature (10.4 °C), precipitation (500–600 mm), and afforestation years (25 years), compared with this study. Broadleaf forests had lower carbon and higher nitrogen and phosphorus contents in the litter [47], whereas coniferous forests produced litter rich in carbon and poor in nitrogen, and had a low litter decomposition rate [48]. Satti et al. [49] pointed out that the leaf litter nitrogen, nitrogen mineralization, and soil nitrogen in coniferous trees were lower than in that of broadleaf trees. Consequently, different plantations in our study showed obvious variations in soil physicochemical properties and litter quality, illustrating that long-term afforestation with different tree species may establish diverse patterns of plant–soil feedback [7,50].

4.3. Effects of Tree Species on Soil Enzyme Activities

Plantations could alter plant biomass, species diversity, litter properties, soil characteristics, and microbial communities [4,46,51], which play an important role in changes in the soil enzyme activities [18,52]. The five plantations of this study had significantly different soil properties and litter quality. A study of soil microbial time dynamics confirmed that SWC, pH, soil phosphorus, leaf litter phosphorus, and the ratio of leaf litter carbon to phosphorus influenced soil enzyme activities by enhancing or inhibiting the growth of soil microbes [21]. Our study showed that soil SC, UE, and ALP in C. korshinskii were higher than those in the other four tree species by 3.97–16.90%, 13.96–36.99%, and 42.56–178.25%, indicating that tree species could directly or indirectly affect soil enzyme activities [20,23]. In the C. korshinskii plantation, improved SWC, BD, and pH created an environment conducive to the growth of microorganisms, and the higher quality litter provided a good material basis for microbial growth and enzyme synthesis. Moreover, high-quality litters and high soil nutrients drove increases in soil animal and microbial abundance [44,51], which enhanced soil enzyme activities [53,54]. In addition, P. orientalis presented lower soil SC, UE, and ALP than C. korshinskii, A. sibirica, P. hopeiensis, P. tabuliformis, and grassland. However, in semi-humid plantations, the soil enzyme activities in P. orientalis stands were higher than that in stands of Sophora davidii (a leguminous shrub) [18], suggesting that P. orientalis may not be suitable for afforestation in arid areas [55], which was also supported by the low litter quality, SWC, and soil nutrients of P. orientalis in this study.

4.4. Response of Soil Nutrients to Chemical Litter Properties and Soil Enzyme Activities

Vegetation played a major role in the change in soil nutrients, and the influence of different vegetation types on soil nutrients was controlled by the litter [40,56]. Nutrient return to the soil through litter decomposition was the main link in the material cycle of the ecosystem [57]. High-quality litter and high soil enzyme activity could accelerate the nutrient release of litter and improve the supply capacity of soil available nutrients for plant growth [54,57]. Therefore, the litter properties and soil enzyme activity determined the level of soil fertility [43,58]. Our results showed that the variations in soil nutrients following long-term afforestation with different tree species were caused by soil TC, OC, TN, AN, AP, and AK, which were significantly positively affected by the LN, LP, UE, and ALP (Table 3). Firstly, the LN and LP represented the litter quality, which decided the decomposition rate and microbial metabolism, thereby affecting the nutrient cycle [59,60]. Laughlin et al. [11] found that the correlation coefficient between leaf litter nitrogen content and soil fertility reached a significantly positive level (0.80), because litter with a high nitrogen content had faster decomposition rate, nitrogen mineralization rate, and nutrient release [38]. Zhou et al. [61] revealed that the loss and return of phosphorus increased significantly with an increase in the total phosphorus concentration in litter. Thus, the LN and LP actively increased soil nutrients, and the synchronous changes in the chemical litter properties and soil nutrients in C. korshinskii and P. orientalis plantations proved that high-quality litter improved soil nutrient succession in dryland plantations, and vice versa [38].
Secondly, the differences in soil carbon, nitrogen, and phosphorus contents were similar to those in soil enzyme activities in different tree species plantations, especially C. korshinskii and P. orientalis, indicating that there was a strong interaction between soil nutrients and enzymes (Table 3). Some studies have reported that soil nutrients are closely related to soil enzyme activities [15,19], and soil enzyme activities have the potential to estimate the decomposition rate and the availability of nitrogen and phosphorus [62]. The leaf litter decay rate, soil dissolved organic nitrogen, and AP significantly increased with increased enzyme activities [20,63]. The improvement in the soil’s physical and chemical properties was conducive to the synthesis of soil enzymes and the maintenance of soil enzyme activities [52,58]. Thus, soil enzyme activities had a positive interaction with soil nutrients (Table 3).
In addition, the significant positive correlation of LN, UE, TN, and AN with LP, ALP, and AP demonstrated that nitrogen and phosphorus had an obvious coupling relationship, which was confirmed by Zhao and Zeng [64], who noted that phosphorus addition strongly altered the impact of nitrogen addition on soil nitrogen and phosphorus transformations. Another nitrogen addition test revealed that the soil’s specific acid phosphatase, N-acetyl glucosaminidase, and oxidative enzyme activities significantly increased with an increase in nitrogen levels [65], which suggested that improved soil nitrogen availability could promote the cycling of phosphorus and other elements. Zhang et al. [19] and Zhang et al. [25] pointed out that phosphorus limitation exists in the Loess Plateau and might become more severe in the future. Soil microbes promote the extraction of restricted elements by regulating the production of soil enzymes and the efficiency of nutrient utilization [21]. The significant positive correlations among TC, OC, TN, AN, LN, and LP; OC, TN, AN, LN, and ALP; and LN, SC, UE, and AP illustrated that soil microbes might tend to consume more carbon and nitrogen to increase the soil phosphorus availability, and alleviate the limitation of phosphorus on the growth of plants and microorganisms, while improving other nutrients in the soil [21,66]. It was also proven that in the C. korshinskii plantation with the highest LN, TC, OC, TN, and AN, the LP, soil ALP and AP was higher than that in other plantations and grasslands (Figure 1, Figure 2 and Figure 3). The LP and ALP had a larger correlation coefficient with soil nutrients than LN and UE (Table 3). Therefore, these results revealed that LP and ALP were the key factors driving soil nutrient changes in dryland plantations limited by phosphorus, which supported our prediction that LP and ALP were important for soil nutrient dynamics.

5. Conclusions

This study emphasized the importance of litter quality and soil enzyme activities in the succession of soil nutrients in dryland plantations. Long-term afforestation led to significant changes in soil nutrients, chemical litter properties and soil enzyme activities. The soil TC in different plantations increased, and soil TP and TK significantly decreased. C. korshinskii significantly increased the soil nutrients (TC, OC, TN, AN, and AK), and its soil AP also increased. However, P. orientalis significantly decreased the soil nutrients (TN, TP, AP, TK, and AK). Therefore, soil nutrients in dryland plantations were significantly affected by tree species. Moreover, 62.2% of the total variation in soil nutrients could be explained by the litter quality and soil enzyme activities, and the LP and soil ALP had a greater effect on soil nutrients than the LN and soil UE. The significant positive correlations of the LP, soil ALP, and AP with the LN, soil UE, TN, and AN further indicated that there was an important coupling relationship between nitrogen and phosphorus, which may promote the phosphorus cycle and alleviate the phosphorus limitation. In short, tree species, LP, and soil ALP determined the changes in soil nutrients in phosphorus-deficient dryland plantations. These findings will help guide the establishment and management of dryland plantations, and it is recommended to choose leguminous tree species for afforestation, such as C. korshinskii, in phosphorus-limited drylands.

Author Contributions

Conceptualization, C.Z. and S.S.; formal analysis, Y.L.; investigation, Y.L. and C.H.; writing—original draft preparation, Y.L.; writing—review and editing, Y.L. and C.H.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20100101), a Major Special Science and Technology Project of Gansu Province (18ZD2FA009), and the National Natural Science Foundation of China (NSFC) (31522013).

Data Availability Statement

The datasets used and/or analyzed in this study are available from the corresponding author on reasonable request.

Acknowledgments

We thank Xiaoxue Dong, Yongjing Liu, Cankun Zhang, Tairan Zhou, Tao Wen, and Guiying Ma for their help in the experiment, and we also thank the staff of the field scientific observation and research station of the mountain ecosystem in Gansu Province.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 12 01153 g001 550
Figure 1. (A–H) indicate different small panels, which are 8 soil nutrient properties. The impact of different vegetation types on soil carbon, nitrogen, phosphorus, and potassium contents: TC, total carbon; OC, organic carbon; TN, total nitrogen; AN, available nitrogen; TP, total phosphorus; AP, available phosphorus; TK, total potassium; AK, available potassium. Different lowercase letters (a–d) indicate significant differences among the six vegetation types (p < 0.05).
Figure 1. (A–H) indicate different small panels, which are 8 soil nutrient properties. The impact of different vegetation types on soil carbon, nitrogen, phosphorus, and potassium contents: TC, total carbon; OC, organic carbon; TN, total nitrogen; AN, available nitrogen; TP, total phosphorus; AP, available phosphorus; TK, total potassium; AK, available potassium. Different lowercase letters (a–d) indicate significant differences among the six vegetation types (p < 0.05).
Forests 12 01153 g001
Forests 12 01153 g002 550
Figure 2. (A–D) Indicate different small panels, which are 4 litter chemical properties. Comparison of the chemical properties of litter from different tree species. LC, litter carbon; LN, litter nitrogen; LP, litter phosphorus; LK, litter potassium. Different lowercase letters (a–e) indicate significant differences among the five plantations (p < 0.05).
Figure 2. (A–D) Indicate different small panels, which are 4 litter chemical properties. Comparison of the chemical properties of litter from different tree species. LC, litter carbon; LN, litter nitrogen; LP, litter phosphorus; LK, litter potassium. Different lowercase letters (a–e) indicate significant differences among the five plantations (p < 0.05).
Forests 12 01153 g002
Forests 12 01153 g003 550
Figure 3. (A–C) indicate different small panels, which are 3 soil enzyme activities. Comparison of the soil enzyme activities of different vegetation types. SC, sucrase; UE, urease; ALP, alkaline phosphatase. Different lowercase letters (a–d) indicate significant differences among the six vegetation types (p < 0.05).
Figure 3. (A–C) indicate different small panels, which are 3 soil enzyme activities. Comparison of the soil enzyme activities of different vegetation types. SC, sucrase; UE, urease; ALP, alkaline phosphatase. Different lowercase letters (a–d) indicate significant differences among the six vegetation types (p < 0.05).
Forests 12 01153 g003
Forests 12 01153 g004 550
Figure 4. RDA estimating the contribution of chemical litter properties and soil enzyme activities to soil nutrients. The total contribution rate of the four axes in the RDA is 62.2%; the red arrows indicate the effect variables and the black arrows indicate the response variables. * indicates significance at the p < 0.05 level.
Figure 4. RDA estimating the contribution of chemical litter properties and soil enzyme activities to soil nutrients. The total contribution rate of the four axes in the RDA is 62.2%; the red arrows indicate the effect variables and the black arrows indicate the response variables. * indicates significance at the p < 0.05 level.
Forests 12 01153 g004
Table
Table 1. Geographical characteristics of different vegetation types in the study site.
Table 1. Geographical characteristics of different vegetation types in the study site.
Vegetation
Types		Afforestation
Time (Years)		Coverage
(%)		Altitude
(m)		Longitude
(° E)		Latitude
(° N)		Slope
Position		Slope
(°)
Grassland—902133104.231435.9867Topslope0
C. korshinskii55902037104.260335.9262Upslope23
A. sibirica50702129104.324835.9697Upslope24
P. hopeiensis50752089104.323435.9633Upslope23
P. orientalis50602136104.324435.9693Upslope28
P. tabulaeformis50852090104.323635.9683Upslope19
Table
Table 2. Soil physical properties of six vegetation types in the study site.
Table 2. Soil physical properties of six vegetation types in the study site.
Vegetation TypesBD (g·cm−3)SWC (%)pH
Grassland1.19 ± 0.02 a14.44 ± 0.16 a8.05 ± 0.01 a
C. korshinskii0.98 ± 0.04 c11.87 ± 0.53 b8.02 ± 0.02 a
A. sibirica1.07 ± 0.02 b6.04 ± 0.14 d7.93 ± 0.04 bc
P. hopeiensis0.99 ± 0.03 c13.89 ± 0.29 a8.05 ± 0.02 a
P. orientalis1.15 ± 0.02 ab5.53 ± 0.21 d7.98 ± 0.01 ab
P. tabuliformis1.08 ± 0.03 b8.75 ± 0.72 c7.87 ± 0.04 c
Notes: Bulk density (BD), soil water content (SWC). Values are mean ± standard error (n = 5). Different lowercase letters within a column indicate significant differences among the six vegetation types (p < 0.05).
Table
Table 3. Correlation coefficients of chemical litter properties, soil enzyme activities, and soil nutrients in plantations.
Table 3. Correlation coefficients of chemical litter properties, soil enzyme activities, and soil nutrients in plantations.
PropertiesLCLNLPLKSCUEALP
LC———————
LN−0.56 **——————
LP−0.340.96 **— ———
LK−0.51 **−0.02−0.05————
SC−0.180.370.420.30———
UE−0.58 *0.91 **0.88 **0.170.52 *——
ALP−0.470.96 **0.94 **−0.080.390.84 **—
TC−0.380.63 **0.55 **−0.20−0.260.170.46
OC−0.160.74 **0.80 **−0.080.74 **0.68 **0.66 **
TN−0.45 *0.88 **0.88 **0.100.55 *0.80 **0.88 **
AN−0.320.79 **0.83 **0.040.460.74 **0.69 **
TP−0.060.330.370.040.410.260.28
AP−0.080.70 **0.80 **0.040.54 *0.60 *0.71 **
TK−0.20−0.02−0.100.090.080.120.08
AK−0.47 *0.64 **0.65 **0.47 *0.58 *0.67 **0.69 **
Notes: ** indicates significance at the p < 0.01 level and * indicates significance at the p < 0.05 level.
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Li, Y.; Han, C.; Sun, S.; Zhao, C. Effects of Tree Species and Soil Enzyme Activities on Soil Nutrients in Dryland Plantations. Forests 2021, 12, 1153. https://0-doi-org.brum.beds.ac.uk/10.3390/f12091153

AMA Style

Li Y, Han C, Sun S, Zhao C. Effects of Tree Species and Soil Enzyme Activities on Soil Nutrients in Dryland Plantations. Forests. 2021; 12(9):1153. https://0-doi-org.brum.beds.ac.uk/10.3390/f12091153

Chicago/Turabian Style

Li, Yage, Chun Han, Shan Sun, and Changming Zhao. 2021. "Effects of Tree Species and Soil Enzyme Activities on Soil Nutrients in Dryland Plantations" Forests 12, no. 9: 1153. https://0-doi-org.brum.beds.ac.uk/10.3390/f12091153

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