*3.1. Effects of Different Forest Types of P. yunnanensis on Soil's Physicochemical Properties* 3.1.1. Soil's Physical Properties

The results of three physical properties of soil under six forest stands are shown in Table 2. The soil bulk density showed an upward trend from the top of the soil that decreased with the increase in soil depth both at the beginning of afforestation (2016) and after two years of afforestation (2018). The interesting phenomenon is that the soil bulk density after two years of afforestation of the six forest types was all lower than that at the beginning of afforestation (2016); the soil bulk density of five *P. yunnanensis* mixed forest types was lower than that of the *P. yunnanensis* pure forest. However, total soil porosity and moisture content showed the opposite trend to BD; specifically, the total porosity and moisture content of six forest types showed a decreasing trend from the surface layer as the soil depth increased. Of course, the total porosity and moisture content of the same layer after two years of afforestation (2018) were higher than those at the beginning of afforestation (2016). Besides this, compared with the *P. yunnanensis* pure forest, the *P. yunnanensis* × C. tetrandra (2:1) (Forest V) mixed forest stand in 0–20 cm soil layer had the lowest bulk density and the highest total porosity and water content after two years of afforestation (Figure S1). In addition, the soil's physical properties of different mixed proportions were also different in the unified mixed forest type. After two years of afforestation (2018), the total soil porosity and water content in Forest I and Forest III were all significantly higher than those in Forest II and Forest IV, respectively. However, at the beginning of afforestation (2016), there was no significant difference in the soil's physical properties among different mixed proportions of the same mixed forest type.

**Table 2.** Description of soil physical properties under six forest stands at 0–20, 20–40 cm, and 40–60 cm (mean ± stand error, *n* = 3).


Notes: BD: bulk density; TOP: total porosity; MC: moisture content. Different capital letters indicate that the indexes of different soil layers of the same forest type are significantly different (*p* < 0.05). Different lowercase letters indicate that there are significant differences in different indexes of different forest types in the same soil layer (*p* < 0.05).

### 3.1.2. Soil's Chemical Properties

Table 3 shows the results of four of the soil's chemical properties under six forest stands. Although the pH values of the three soil layers were not significantly different (*p* > 0.05), the soil of the six forest types were weakly acid, and its pH value was in the range of 4–7. Compared with the *P. yunnanensis* pure forest, *P. yunnanensis* mixed with *A. nepalensis*, *Q. acutissima, C. tetrandra*, and *P. yunnanensis* reduced the soil's acidity. The soil's TN, TP, and AP content from most of the forest stands significantly decreased with the deepening of the soil depth (*p* < 0.05). Interestingly, the TP and AP content after two years of afforestation (2018) were higher than those at the beginning of afforestation (2016). In addition, after two years of afforestation, Forest III had the highest mean TN content of both the soil depths, Forest V had the highest TP content of both the soil depths, and Forest I had the same AP content at both the soil depths (Figure S2). Besides this, the soil chemical

properties of different mixed proportions were also different in the unified mixed forest type. After two years of afforestation (2018), the soil's TN, TP, and AP content in Forest I and Forest III were all significantly higher than that in Forest II and Forest IV, respectively.

**Table 3.** Description of the soil's chemical properties under six forest stands at 0–20, 20–40, and 40–60 cm (mean ± stand error, *n* = 3).


Notes: Available P: available phosphorus content; TP: total phosphorus content; TN: total nitrogen content. Different capital letters indicate that the indexes of different soil layers of the same forest type are significantly different (*p* < 0.05). Different lowercase letters indicate that there are significant differences in different indexes of different forest types in the same soil layer (*p* < 0.05).

### *3.2. Effect of Different Forest Types of P. yunnanensis on Soil Enzyme Activity*

The results of soil enzyme activity in six different *P. yunnanensis* forest stands are shown in Table 4. The soil urease activity levels of *P. yunnanensis* forest types at two years of afforestation (2018) were all higher than those at the beginning of afforestation (2016), among which the urease activity of 0–20 cm surface soil was the highest. After two years of afforestation, the surface soil urease activity of Forest I was the highest, at 563.50 μg/g·24 h, which was significantly higher than that at the beginning of afforestation (2016) (*p* < 0.01) (Figure S3A). The soil sucrase activity of six different *P. yunnanensis* forest types all decreased significantly with the increase in soil depth (*p* < 0.05) both at the beginning of afforestation (2016) and after two years of afforestation (2018). The order of sucrase activity in different soil layers both at the beginning of afforestation (2016) and after two years of afforestation (2018) was 0–20 cm > 20–40 cm > 40–60 cm. As shown in Figure S3B, the sucrase activity of the surface soil after two years of afforestation (2018) of Forest III was significantly higher than that of the surface soil at the beginning of afforestation (2016) (*p* < 0.01). The soil catalase activities of the six different forest types also showed a decreasing trend with the increase in soil depth both at the beginning of afforestation (2016) and after two years of afforestation (2018). After two years of afforestation (2018), the forest soil surface layer (0–20 cm) had the highest catalase activity, and Forest I had the highest mean catalase activity, which was 8.12 mg/g·24 h, and Forest VI had the lowest the catalase activity, which was 7.11 mg/g·24 h. As shown in Figure S3C, in the 0~20 cm soil layer after two years of afforestation (2018), there was no difference in the catalase activity of the soil under the same forest type. Obviously, the three enzyme activities of different mixed proportions were also different in the unified mixed forest type. After two years of afforestation, the three enzyme activities in Forest I and Forest III were all significantly higher than that in Forest II and Forest IV, respectively.

**Table 4.** Description of soil enzyme activities under six forest stands at 0–20, 20–40, and 40–60 cm (mean ± stand error, *n* = 3).


Notes: Ure: urease activity; Suc: sucrase activity; Cat: catalase activity. Different capital letters indicate that the indexes of different soil layers of the same forest type are significantly different (*p* < 0.05). Different lowercase letters indicate that there are significant differences in different indexes of different forest types in the same soil layer (*p* < 0.05).

### *3.3. Principal Component Analysis and Cluster Analysis of Soil Fertility in Different Forest Types*

As shown in Table 5, the first two components had eigenvalues of magnitude >1. The variance contribution rate of the principal component 1 (PC1) is 80.755%, which explains most of the variation in the data, the variance contribution rate of the principal component 2 (PC2) is only 11.770%, and the cumulative variance contribution rate of the PC1 and PC2 is 92.525%, which basically explains all the variation in the data. Taking the principal component scores of different forest types as a new index to evaluate the soil fertility quality, it can be seen that the soil fertility quality of different forest types is ranked as follows: 2018-V > 2018-I > 2018-III > 2018-IV > 2018-II > 2018-VI > 2016-II > 2016-III > 2016-IV > 2016-VI > 2016-V > 2016-I.


**Table 5.** Score and overall score of principal components influencing each index of soil fertility.

Taking Euclidean distance as a measure of the sudden fertility difference of different forest types, the results of the systematic clustering of each forest type by using the shortest distance method are shown in Figure 3. The twelve forest types were grouped into four categories, and 2018-I was separately grouped into one category; the soil quality was first class. 2018-II, 2018-III, and 2018-V were clustered into one class, and the soil quality was second class. 2018-IV, 2018-VI, and 2016-II were clustered into one class; the soil quality was third class. 2016-III, 2016-IV, 2016-V, 2016-I, and 2016-VI were clustered into one class; the soil quality was fourth class.

**Figure 3.** Hierarchical cluster analysis of the soil fertility of different forest types.

Pearson correlation analysis was used to examine the relationships among these properties to reduce redundancy (Table 6). Although the soil bulk density was not positively correlated with pH, it was significantly negatively correlated with other physical and chemical properties and enzyme activities (*p* < 0.01). This indicated that the larger the soil bulk density is, the worse the soil structure is, and the more compact soil is. This phenomenon will also lead to poor soil ventilation performance, which is not conducive to the decomposition and transformation of litter nutrients on the surface of forest land and thus will reduce the mass fraction of forest soil nutrients. There was a significant positive correlation between the soil water content and TN, AP, TP, and total porosity (*p* < 0.01). In addition, the contents of total nitrogen, total phosphorus and available phosphorus, total porosity, and moisture content were significantly positively correlated with the activities of urease, invertase, and catalase (*p* < 0.01).

**Table 6.** Pearson correlation coefficients among soil properties.


Notes: \*\*, Significant correlation at *p* < 0.01. BD, soil bulk density; TOP, total porosity; MC: moisture content; TN, total nitrogen; TP, total phosphorus; AP, available phosphorus; Ure, urease; Cat, catalase; Suc, sucrase.

### *3.4. Growth Status of Various Tree Species after Two Years of Afforestation*

By selecting the *P. yunnanensis* of four forest types and measuring its ground diameter and plant height, we found that the *P. yunnanensis* mixed with *A. nepalensis* or *Q. acutissima* has the highest ground diameter and plant height, which indicates that the *A. nepalensis* and *Q. acutissima* are beneficial to the growth of *P. yunnanensis* (Figure 4).

**Figure 4.** The growth status of *P. yunnanensis* of four forest types after two years of afforestation.

### **4. Discussion**

Soil is the carrier of forest ecological processes and the material base of forest survival. Soil nutrient status directly affects the growth and metabolism of trees [32]. The mixing of different tree species inevitably leads to changes in the forest ecosystem, which directly affects the soil nutrient flow and material circulation, and the soil nutrient content also changes. Mixed forest stands are different, which leads to certain differences in different soil physical and chemical properties of different forest stands [33].

Li [15] and Lai [34] showed that different forest types have a significant impact on soil properties, such as soil's physical and chemical properties. Different forest types lead to certain differences in surface litter reserves and their composition, tree root growth and development, and the litter decomposition rate, resulting in different physical properties of the soil of different forest stands [35]. The soil bulk density affects the circulation, storage, and distribution of water, moisture, and heat in trees. Small bulk density means loose soil texture and good structure. On the contrary, soil that is compact lacks a granular structure. This study found that the soil bulk density of the six forest types increased with increasing soil depth, which is consistent with previous studies [36,37]. Forest III and Forest V had the lowest bulk density, and Forest VI had the highest bulk density on the soil surface. This may be due to the single tree species of the *P. yunnanensis* pine pure forest, the slow decomposition of litter, and the soil becoming compact, which is not conducive to the long-term utilization of forest land. It indicated that *P. yunnanensis* pine mixed with other species had better soil infiltration. *Q. acutissima* is a broad-leaved deciduous tree species, the litter is easy to decompose, and the activity of the microbial community in the decomposition process can reduce the compactness of the soil. Soil moisture is an important component of the forest, which actively participates in the transformation and metabolism of soil substances [38]. In our study, the surface soil water content was higher than that of the deep layer, which may be because the litter layer of the mixed forest is thicker and the evaporation is less, so there is more surface soil moisture storage. The soil water holding capacity was different among different forest types, and the five mixed forests were higher than the *P. yunnanensis* pure pine forest, which was caused by the difference in soil porosity.

The changes in forest type, stand age, and land use can also affect soil's chemical properties significantly [39–42]. Generally, the litter of forest plants is distributed on the soil's surface, and a large number of nutrient elements are released on the surface of the soil. With the increase in soil depth, the litter is less and less [43,44]. Therefore, in this study, the total soil nitrogen, total phosphorus, and available phosphorus of each forest stand show the phenomenon of surface accumulation, which was consistent with the previous research [45]. After two years of afforestation (2018), the soil's TN, TP, and AP content in Forest I and Forest III were all higher than that in other forest types. The reason for this was that the nitrogen content in the middle leaf of *A. nepalensis* is higher than that in the leaves of other plants, and the degradation of leaf litter provides a large amount of nitrogen to the soil and improves soil fertility [46]. Forrester et al. [47] found that nitrogen content and nitrogen assimilation in the soil litter of mixed forests were higher than those of pure forests, which led to the content of available nitrogen and phosphorus in the soil of mixed forests being higher than that of pure forests. However, the soil's TN, TP, and AP content in Forest VI were lowest in six forest types; the reason is that the needles of *P. yunnanensis* have a coarse and hard texture, high cellulose content, waxy epidermis, and poor water permeability, so it is difficult to decompose and transform, which affects the accumulation of organic matter in soil [48]. This study also showed that the difference between TN and TP in mixed forests was basically not significant because soil TP mainly comes from rock weathering and litter decomposition, which is a long process [49]. TN mainly comes from the degradation of litter; the difference in TN among forest types is basically not significant, which may be related to the close litter and slow decomposition. In summary, the *P. yunnanensis* mixed forest had improved the soil quality, which was consistent with the research of Wang [50]. They showed that mixed forests promote increases in soil organic matter, N and P content, improve soil nutrient status, and help to sustain soil fertility.

In this study, after two years of afforestation, three enzymes of five *P. yunnanensis* pine mixed forest types were higher than *P. yunnanensis* pure pine. The results showed that the mixed forest was beneficial to the accumulation and decomposition rate of forest litter, increased the amount of nutrient return, improved nutrient availability, and accelerated soil nutrient mineralization [51]. Pure forest could inhibit the decomposition activities of soil microorganisms due to its allelopathic effect. Interestingly, the three kinds of soil enzyme activities in the surface soil (0–20 cm) of different mixed models were higher than those in the deep soil (40–60 cm). Yang et al. also found that the soil urease, sucrase, and catalase enzyme activities decreased with the increase in soil depth before and after afforestation, which was consistent with the research results of Yang [52]. The reasons for this vertical decrease in soil enzyme activity are that there were more litter and microorganisms in

the surface soil of the mixed forest, and the physiological and biochemical reactions of soil microorganisms and various enzymes were intense, while there were less litter and microorganisms in the deep soil, and the physiological and biochemical reactions were relatively stable [53]. Another reason is that the surface soil has a rich root system that penetrates the whole topsoil layer so that the enzyme activity in the surface soil is higher than that in the deep soil [54]. Sucrase comes from plant roots and microorganisms, which can catalyze the hydrolysis of sucrose into glucose and sucrose, which plays an important role in soil carbon and nitrogen cycling [55]. Catalase can promote the oxidation of various compounds by hydrogen peroxide, reduce the toxic effect of hydrogen peroxide in soil, and also reflect the total respiratory intensity in soil. In this study, different mixed modes had no significant effect on catalase activity, which may be related to tree species types and tree species growth stages. Zhou et al. [56] proved that the soil sucrase and catalase activities increased with the increase in forest age of the top Chinese prickly ash green food. In our study, the three enzyme activities in the topsoil of the six forest types after two years of afforestation (2018) were higher than those at the beginning of afforestation (2016). Therefore, long-term monitoring of soil enzyme activity in different mixed forests can be carried out in the future. Urease is a key enzyme in nitrogen cycling, which can catalyze the hydrolysis of urea. NH3 formed by hydrolysis is one of the nitrogen sources of plants, and its activity can reflect the nitrogen supply capacity and level of soil [57]. In our study, after two years of afforestation, Forest I had the highest urease activities, which is mainly due to the presence of the nitrogen-fixing plant *A. nepalensis*. Nitrogen-fixing plants can often significantly increase soil organic matter and soil nitrogen [58]. For example, Wang et al. showed that the organic matter and nitrogen contents in the surface soil of an artificial forest of nitrogen-fixing trees were 40–50% and 20–50% higher than those of non-nitrogen-fixing trees, respectively [59]. At the beginning of afforestation (2016), there was no significant difference in soil indexes between mixed forest and pure forest. This may be because even though the forest types are different, this is a new mixed forest and their original soil conditions are basically the same.

Correlations were also found between these soil indices. There was a correlation between soil physicochemical properties and enzyme activities [60,61]. In this study, correlation analysis of physicochemical properties and enzyme activities of surface soil of six forest types was carried out. Soil enzymes are very sensitive to environmental changes, such as pH, temperature, and water content [62]. In our study, there was no significant negative correlation between soil pH and three kinds of soil enzyme activities, which indicated that if the soil's acidity is too strong, it will inhibit the enzyme activity. In addition, we selected ten soil fertility indexes of six forest types for principal component analysis to evaluate the soil quality of different forest types. Due to the different types of forest vegetation, there are some differences in the reserves and composition of surface litter, the growth and development of tree roots, and the decomposition rate of litter, resulting in the difference in the soil's nutrient content in different forest types [63]. In our study, through the comprehensive evaluation and ranking of soil quality of different *P. yunnanensis* pine mixed forest types by using the method of principal component analysis and cluster analysis, it was found that the soil quality of Forest V, Forest III, and Forest I were higher after two years of afforestation. The fundamental reason is that the species composition and biological characteristics of different forest types are different, so the quality, quantity, and decomposition rate of the litter of different forests are different, which affects the soil's nutrient content and distribution in different forest types. This indicated that the mixed pattern of *P. yunnanensis* pine growing for a certain number of years could improve the soil's nutrient content compared with the *P. yunnanensis* pure forest. On the other hand, compared with the growth status of *P. yunnanensis* under the *P. yunnanensis* pine pure forest, the growth status of *P. yunnanensis* pine under the three mixed patterns was relatively better, and the growth status of *P. yunnanensis* pine in Forest III was the best. This result was consistent with the result that the soil quality of Forest I, Forest II, and Forest V was better than that of Forest VI.

### **5. Conclusions**

Interplanting different forests under *P. yunnanensis* forests can increase the soil quality, which is significant for improving the ecological environment of *P. yunnanensis* mixed forests. The results of this study showed that, compared with the *P. yunnanensis* pure forest, the *P. yunnanensis* × *A. nepalensis* mixed forest could improve soil physicochemical properties. The ratio of row spacing between *P. yunnanensis* and the selected mixed tree species was 2:1, which could make more rational use of soil nutrients and promote the growth of *P. yunnanensis*. Among them, such two planting patterns as *P. yunnanensis* × *Q. acutissima* mixed forest and *P. yunnanensis* × *A. nepalensis* have outstanding soil improvement effects, and they are worthy of promotion and application in *P. yunnanensis* forests in areas with insect pests.

Although the soil indexes of different *P. yunnanensis* mixed forests were measured in this study, their functions were not fully displayed because they were still in the young forest stage. Therefore, the future soil quality evaluation of the mixed forests needs a lot of research and long-term positioning observation. This study found that the mixed pattern of *P. yunnanensis* pine at the early stage of afforestation was affected by factors such as growth period and external environment, so proving its feasibility is a long-term process. Follow-up studies will continue in the later stage of the project to verify the effect of the *P. yunnanensis* pine mixed forest model and provide excellent companion trees for the sustainable management of *P. yunnanensis* pine mixed forests.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/10.3390/d14030214/s1, Figure S1: Comparison of the physical properties of surface soil (0–20 cm) at the beginning of afforestation (2016) and after two years of afforestation (2018). Figure S2: Comparison of the chemical properties of topsoil (0–20 cm) in different forest types at the beginning of afforestation (2016) and after two years of afforestation (2018). Figure S3: Comparison of the enzyme activities in the surface soil (0–20 cm) of different forest types at the beginning of afforestation (2016) and after two years of afforestation (2018).

**Author Contributions:** Conceptualization, funding acquisition, and project administration, N.Z. and B.Y.; data curation. Z.L. and L.L.; formal analysis, C.L., Z.Z. and M.J.; investigation, S.Z. and M.J.; methodology, B.Y. and J.Y.; resources, C.L., S.Z. and N.Z.; software, L.L. and J.Y.; supervision, Z.Z. and Z.L.; validation, L.L. and S.Z.; visualization, C.L., B.Y. and J.Y.; writing—original draft, C.L.; writing—review & editing, C.L. and N.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China (31760210) and the Key Project of Yunnan Applied Basic Research Program (Grant No.202101AS070009; 2018FG001- 010).

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

