Seasonal Dynamics of Soil Enzymatic Activity under Different Land-Use Types in Rocky Mountainous Region of North China

Abstract

To reveal the effects of different land-use types on soil enzyme activities, soil samples were collected from 0–10, 10–20 and 20–30 cm soil layers to compare and analyze soil β-glucosidase (BG), urease (URE), protease (PROT) and catalase (CAT) activities in farmland (FL), abandoned land (AL) and three plantation forests: Platycladus orientalis (L.) Franco (PO), Robinia pseudoacacia L. (RP) and Quercus variabilis Bl. (QV) in the rocky mountainous region of North China. The results showed that the soil enzyme activities varied significantly under different land-use types, and the interannual mean values of FL and PO were remarkably higher than those of other land uses in the 0–30 cm soil layer, in which the soil BG and URE activities of FL were 22% and 12% higher than those of AL, and 428% and 179% higher than those of QV, respectively; the soil PROT and CAT activities of PO were 66% and 23% higher than those of AL, and 479% and 113% higher than those of QV, respectively. Soil BG, URE and PROT activities were all higher in June and lower in December, while soil CAT activity was slightly lower in June. The soil enzymatic activities all showed a notable decrease with the depth of the soil layer. Soil BG, URE, PROT and CAT activities were remarkably (p < 0.01) or significantly (p < 0.05) positively correlated with available nitrogen, available phosphorus, available potassium, dissolved organic carbon (DOC), NO₃⁻-N, soil organic carbon, water content, clay and silt volume fraction, and significantly negatively correlated with sand volume fraction. Soil DOC and pH were important factors influencing soil enzymatic activity, implying that changes in soil enzymatic activity under different land-use types may be the result of a combination of temperature, moisture and plant type. In conclusion, PO plantations are conducive to improving the physicochemical and biological properties of soil and enhance soil fertility, which is a reasonable land-use method to achieve sustainable development in the rocky mountainous region of North China.

1. Introduction

As the most active among soil organic components, soil enzymes take part in all biochemical processes in the soil environment, and can reflect the quality and health of the soil ecosystem and environment [1,2,3]. They are mainly derived from plant and animal residues, plant roots and microorganisms [4,5,6]. As an indicator of microorganisms, soil enzyme activity is an important factor in controlling organic matter decomposition and nutrient movement, and enzyme responses can serve as an early alert about soil physicochemical and biological variations [7]. Soil enzyme activities change with the nutrient requirements and metabolic activity of soil microorganisms [8,9]. For example, β-glucosidase (BG) and urease (URE) take part in the carbon and nitrogen cycles, respectively [10], and catalase (CAT) and protease (PROT) are essential for the redox processes of macromolecules contained in soil organic matter [11]. The critical roles of soil enzymes in different nutrient cycles are well demonstrated [12,13]. However, there are still some blind spots in the response of soil enzymatic activity to seasonal and land-use changes.

Land-use types and histories can alter soil microbial activity and its community structure [9] by influencing soil organic matter content [14]; soil physical structure [15] as well as the type, amount and residue of vegetation apoplast, and all these changes govern soil enzymatic activity [16,17]. It has been shown that soil enzymatic activity varies notably among land-use types. For instance, microbial communities can be governed by different land-use practices by altering soil environmental factors [18]. The content of soil mineral elements, soil enzymatic activity and soil fungal communities in karst areas are obviously controlled by land-use practices and soil depth [19]. It was also noted that the impact of different land-use types on soil enzymatic activity in the Minjiang River Basin showed the pattern secondary forest > plantation forest > scrub > sloping land [20]. Additionally, the biological and geochemical cycles of soil elements in terrestrial ecosystems may be influenced by seasonal dynamics, particularly temperature and precipitation [21]. It has been reported that temperature and precipitation are the main factors regulating soil enzymatic activity [22]. Temperature is a decisive factor for the physicochemical dissolution of soil organic matter and the depolymerization of macromolecules [23], and higher air temperatures lead to higher soil temperatures and lower soil moisture [24]. Precipitation, as another vital climate change driver, can alter the structure and composition of soil microbial communities, and its variation can alter soil enzymatic activity by altering nutrient inputs to soil from plants [25]. Therefore, temperature, precipitation and their interaction profoundly affect soil enzymatic activity. However, little is known about the regulation mechanisms of land-use types, seasonal dynamics and their interactions on soil active C and N turnover. Clearly revealing the response of soil enzyme activities to different land-use types would not only help to understand the changes of soil enzyme activities, but would also be favorable for further elucidating the function and stability of plantation ecosystems.

The rocky mountainous region of North China, as one of the key areas of forest ecological engineering in China, is an important ecological barrier of the North China Plain [26]. This area has poor soils, drought and water shortage, severe soil erosion and numerous unplanted or barren limestone hills [21]. The land-use types in this region mainly include farmland, abandoned land and artificial forest, and it is a typical area to study land-use change. At present, the research reports in this region mainly focus on the effects of different plantation types and tree ages on soil active carbon and nitrogen and greenhouse gas emissions, while little is known about the effects of land-use types and seasonal changes on the pivotal enzyme activities of soil carbon and nitrogen cycles. We hypothesized that land-use types would significantly affect soil enzyme activities by influencing inputs of litter and root exudates, as well as microclimate, and that seasonal dynamics coupled with land-use types would have complex effects on soil enzyme activities. We studied the seasonal characteristics of soil BG, URE, PROT and CAT activities under three typical land-use types; the results of this study are significant for revealing the relationship between enzyme activities and soil physicochemical properties as well as identifying the controlling factors. Furthermore, our aims are to improve soil quality, select reasonable land-use modes and provide a basis for sustainable land development in this region.

2. Materials and Methods

2.1. Study Area and Soil Sampling

In Jiyuan City of Henan Province, the study area is part of the Yellow River Xiaolangdi Forest Ecosystem Location Research Station (35°01′ N, 112°28′ E) of the Chinese Forest Ecosystem Network, which is located in the middle reaches of the Yellow River and close to the Taihang Mountains. It has a warm temperate continental monsoon climate with hot and rainy summers and cold and dry winters. The annual average temperature is 13.1 °C, the highest is 32 °C and the lowest is −4 °C, and the annual sunshine hours are above 2300 h. The annual average rainfall is 641.7 mm, which is unevenly distributed during the year, mostly concentrated in July to September, accounting for more than 60% of the annual rainfall. The soil types are brown loam and Alfisol.

Three typical land-use types were selected as farmland (FL), abandoned land (AL) and artificial forest (i.e., Platycladus orientalis (L.) Franco (PO), Robinia pseudoacacia L. (RP) and Quercus variabilis Bl. (QV)). The farmland in this region is dominated by winter rape and summer maize rotation, treated with compound fertilizer and farm organic manure. Compound fertilizer containing 15% nitrogen (N), 15% phosphorus (P) and 15% potassium (K) is inputted at ca. 500 and 200 kg ha^–1 before sowing (i.e., at the middle of October and April, respectively). The main raw material composing farm organic manure is animal manure, which is spread according to farmers’ habits and crop growth situation. The AL had been abandoned for 1 year, adjacent to the Locust plantation. All land-use types are in close proximity (5 km apart), having similar slope position and aspect. Details of sampling lands are given in Table 1.

According to the principle of proximity arrangement, three 10 m × 10 m fixed sample squares were set up in each sampling plot, totaling 15 sampling squares. Five sampling points were set by the grid-based sampling method in each square, and soil samples were collected from three soil layers (0–10, 10–20 and 20–30 cm). Fresh soil was sieved through a 2 mm sieve and refrigerated at 4 °C until analysis.

2.2. Analysis of Soil Enzymatic Activity

Soil BG activity was evaluated by the nitro-phenol-colorimetric method and expressed as mg ρ-nitrophenol per g of dry soil after 1 h of incubation [27]. Soil URE activity was determined according to the method described by Paudel et al. [28], reported as mg NH₄⁺-N per g dry soil after 1 d of incubation. Soil PROT activity was detected by the ninhydrin colorimetric method and expressed as milligrams of glycine produced after enzymatic reaction per gram of soil after 1 day of incubation [29]. Soil CAT activity was conducted by KMnO₄ titration method with H₂O₂ as substrate, expressed in cm³ of 0.1 mol KMnO₄ dm⁻³ solution titrated per g of dry soil after 1 h of incubation [30].

2.3. Analysis of Soil Physicochemical and Biological Properties

Soil dissolved organic carbon (DOC), dissolved organic nitrogen (DON), ammonia nitrogen (NH₄⁺-N) and nitrate nitrogen (NO₃⁻-N) contents were determined by leaching 5 g of fresh soil (soil:liquid = 1:5) with 1 mol dm⁻³ KCL solution using a SKALAR flow analyzer (HQ-Skalar Analytical B.V., Breda, Netherlands). Soil TN content was determined by automatic elemental analyzer EuroEA3000 (Eurovector, Pavia, Italy). Soil available nitrogen (AN), available phosphorus (AP) and available potassium (AK) contents were determined by the alkaline diffusion method, sodium bicarbonate leaching-molybdenum antimony anti-colorimetric method and ammonium acetate leaching-flame photometer FP6450 (Hinotek, Ningbo, China) method, respectively. Soil microbial biomass carbon (MBC) and nitrogen (MBN) contents were determined by chloroform fumigation and extraction method [31]. Soil pH, moisture content and bulk density were determined as described by Bao Shidan [32]. The soil basic physical and chemical properties were given in Gong et al. [21].

2.4. Statistical Analysis

Multivariate analysis of variance (MANOVA) was used to analyze the influences of land-use type, season, soil layer and their interactions on soil BG, URE, PROT and CAT activity. The relationship between soil enzymatic activity and other soil properties was evaluated by Pearson correlation analysis. Stepwise multiple-regression analysis was conducted to determine the main parameters influencing the soil enzymatic activity. All statistical analysis was conducted in SPSS 20.0 software (IBM, New York, NY, USA).

3. Results

3.1. Seasonal Dynamics of Soil β-Glucosidase and Urease Activities under Different Land-Use Types

Soil BG and URE activities in the 0–30 cm soil layer varied remarkably (p < 0.05) under different land-use types, both showing the lowest level in QV (Figure 1a) (Table 2). The variation of soil BG activity under the five sampling sites ranged from 0.02 to 2.32 mg ρ-nitrophenol g⁻¹ h⁻¹, and its interannual mean values from the 0–30 cm layer showed an overall variation pattern of FL > PO > AL > RP > QV (Figure 1a), among which the activity in FL soil was 8%, 22%, 107% and 428% higher than in PO, AL, RP and QV, respectively. Under the five sampling sites, the variation of soil URE activity ranged from 0.11 to 0.70 mg NH₄⁺-N g⁻¹ h⁻¹, and the mean values showed an overall pattern of PO (0.44 mg NH₄⁺-N g⁻¹ h⁻¹) > FL (0.33 mg NH₄⁺-N g⁻¹ h⁻¹) > AL (0.29 mg NH₄⁺-N g⁻¹ h⁻¹) > QV (0.12 mg NH₄⁺-N g⁻¹ h⁻¹) ≈ RP (0.11 mg NH₄⁺-N g⁻¹ h⁻¹) from the 0–30 cm layer, and the soil URE activity of PO was 26%, 36%, 74% and 75% higher than that of FL, AL, QV and RP, respectively (p < 0.05) (Figure 1b).

There was an obvious seasonal pattern in soil BG and URE activities under different land-use types (p < 0.05) (Table 2). Soil BG activity showed a unimodal trend under all five sites, with the highest activity in June and the lowest in December, while soil URE activity showed higher levels in June or September and the lowest level in December (Figure 1).

With the increase of soil depth, most of the soil BG and URE activities tended to decrease, showing enrichment in the 0–10 cm soil layer (Figure 1) (p < 0.05). For example, in June, soil BG activity in the 0–10 cm soil layer was 1.5–3.4 times that in the 20–30 cm soil layer. In September, soil URE activity in the 0–10 cm soil layer was 1.2–2.2 times than in the 20–30 cm soil layer (Figure 1).

3.2. Seasonal Dynamics of Soil Protease and Catalase Activities under Different Land-Use Types

Different land-use types significantly influenced soil PROT and CAT activities in the 0–30 cm layer (p < 0.05) (Table 2), with both showing the lowest values in QV (Figure 2). Soil PROT activity ranged from 1.72 to 155.70 μg glycine g⁻¹ h⁻¹, and its interannual mean values showed an overall variation pattern of PO > FL > RP > AL > QV from the 0 to 30 cm soil layer (Figure 2a). Among them, the PROT activity of PO was 31%, 39%, 40% and 83% higher than that in FL, RP, AL and QV, respectively (p < 0.05) (Figure 2a). Soil CAT activity ranged from 0.32 to 1.51 cm³ of (0.1 mol KMnO₄ dm⁻³) g⁻¹ h⁻¹, and its interannual mean values in the 0–30 cm layer showed the trend of RP > PO > FL > AL > QV, where CAT activities in RP, PO, AL and FL soils were 116%, 113%, 72% and 10% higher than that in QV, respectively (Figure 2b).

Soil PROT and CAT activities showed notable seasonal patterns under different land-use types (p < 0.05) (Table 2). Soil PROT activity was higher in March or June and lower in September and December. The CAT activity of FL soil in March was significantly higher than that in other months, while that in the RP soil showed the lowest level in June. However, soil CAT activity had no significant seasonal variation in AL, PO and QV (Figure 2). With increasing soil depth, soil PROT and CAT activities of all land-use types showed a decreasing trend in December, while FL, AL and QV had no remarkable change in other months.

3.3. Correlation Analysis of Soil Enzymatic Activity and Soil Physicochemical Properties

As shown in Figure 3, there was a highly significant positive correlation among soil BG, URE, PROT and CAT (p < 0.01). Soil BG, URE, PROT and CAT activities were positively remarkable correlated with AN, AP, AK, DOC, NO₃⁻-N, SOC, water content, clay and silt volume fractions (p < 0.05), and notably negatively correlated with sand volume fraction (p < 0.05). In addition, soil BG, PROT and CAT activities were all positively significant correlated with DON, MBC and TN contents (p < 0.05). Soil BG, URE and PROT activities were all positively correlated with NH₄⁺-N (p < 0.05).

In order to further analyze the main influencing factors of four soil enzyme activities, we conducted stepwise regression analysis. Soil BG, URE, PROT and CAT activities were influenced by different factors (Table 3). DOC was the key factor affecting the activities of soil BG and PROT, accounting for 51% and 62% of the variations, respectively. Secondly, the activities of BG and PROT were affected by AK and MBC, respectively, with the contribution rates of 10% and 5%. Soil URE activity was mainly regulated by soil silt volume fraction and pH, which contributed 29% and 8% of the variations, respectively. Soil pH could explain 47% of the variation in soil CAT activity. In addition, soil water content was the main factor influencing the activities of soil BG (2%), URE (7%), PROT (5%) and CAT (9%) activities.

4. Discussion

Soil enzymes, as biologically active indicators of soil quality, not only reflect the basic condition of the soil, but are also influenced by soil physicochemical properties, temperature, moisture and vegetation type [33,34,35]. Therefore, changes in land-use types, seasons and soil layers are able to induce differences in soil enzymatic activity. In the present study, soil BG, URE, PROT and CAT activities differed remarkably among land-use types (Figure 1 and Figure 2), and all four soil enzyme activities were higher in FL, AL and PO plantations and the lowest in QV plantations, which is not consistent with the results of Silva et al. [36]. In addition to different climate type and soil conditions, artificial fertilization as well as plant characteristics were the main reasons for this phenomenon [37]. FL may have indirectly increased its soil enzymatic activity due to the artificial fertilizer application that inputs large amounts of nutrients to the soil and promotes soil microbial colonization and plant root growth, which are the main sources of soil enzymes released by soil microorganisms and secreted by plant roots [38]. The higher enzymatic activity in the AL soil may have been due to the fact that some crop stubble still remained in the soil, and the stubble decomposition rate reaches its highest value after one year of abandonment, prompting microorganisms to release large amounts of nutrients, enzymes and other active substances into the soil [39]. All four soil enzyme activities were higher in the PO plantation, in contrast to other studies [40], mainly due to their well-developed root system and large annual apoplastic volume [26], lower soil bulk density and adequate source of soil nutrients [21], thus indirectly increasing soil enzyme activities. Meanwhile, the four soil enzyme activities under the QV artificial forest were all the lowest, mainly because of its poor physical structure, such as lower soil bulk density and higher sand volume fraction, which lead to poor water and fertilizer retention abilities [21], and thus reduced soil microbial activities and the production of enzymes.

In this study, soil BG, URE, PROT and CAT activities were characterized by notable seasonal dynamic changes. Soil BG and URE activities were higher in summer and lower in winter under all land-use types, and all showed a highly positive correlation with soil water content, probably because the higher temperatures in summer enhanced the activity of soil microorganisms and frequent rainfall promoted enzyme mobility [23]. In addition, the rapid plant growth and increased root secretion in summer [41] may have significantly increased soil BG and URE activities. In winter, low temperature and dry climate lead to very slow plant growth and low microbial activity, which notably reduced soil organic matter turnover and mineralization rates [21], indirectly reducing soil BG and URE activities. However, the seasonal patterns of the four soil enzyme activities in this study were not entirely consistent, implying that they may be regulated by different factors. Soil PROT activity was mainly influenced by soil DOC, and was higher in March and lower in September. In spring, when the temperature rose, frozen soil melted and released large amounts of C and N nutrients to the soil, which stimulated microbial growth and thus indirectly increased soil PROT activity; in autumn, plants still needed to absorb nutrients from the soil, which made it easy for soil microorganisms to form a competitive relationship [42]. Soil CAT activities were affected by pH and water content, with persistent high temperature and frequent rainfall in summer causing a decrease in activity, while soil pH did not vary significantly throughout the seasons, so there were no notable changes in soil CAT activities between March, September and December.

With the increase of soil depth, soil BG, URE, PROT and CAT activities showed a decreasing trend under the five land-use types (Figure 1), showing superficial enrichment, consistent with the results of Song et al. [43], which is mainly due to the long-term deposition of highly polymerized plant material (especially leaves) and its decomposition, that increases the content of humus and organic matter in the superficial soil and improves the superficial soil structure. In the present study, it was found that the surface soil had less bulk, its soil aeration and water retention were good, and its soil organic matter content was high [21], which provided a suitable living environment and sufficient nutrient sources for microbial growth and enhanced microbial metabolism, resulting in higher enzymatic activity in the surface soil. In addition, all four enzyme activities of the soils in our paper were notably positively correlated with soil AN, AP and AK contents, while the AN, AP and AK contents in top soil were higher than those in the deep soil [21], indicating that the top soil can provide more substrates for microbial metabolism and thus higher enzyme activities. In the vertical spatial variation of the soil profile, soil enzymatic activities are more susceptible to the variations of soil physical properties and available substrates.

5. Conclusions

Different land-use types had remarkable influences on soil enzyme activities, showing higher values in FL cultivation and PO plantation. However, the mechanism behind this phenomenon is not entirely consistent. The high activity in farmland is mainly attributable to high fertilizer inputs and annual agricultural production, while that in PO plantation is mainly caused by its large amount of litter and well-developed root system. Soil BG, URE and PROT activities were found to be very sensitive to the changes of soil temperature and moisture, and all showed high activities in summer. The activities of these enzymes were closely related to soil physicochemical and biological properties. This suggests that, in general, soil enzyme activities can be used as an important indicator of changes in soil quality. In view of its high quality and strong adaptability, Platycladus orientalis can be used as the dominant tree species in the barren rocky mountainous region of North China.

Author Contributions

Conceptualization, Y.K. and Y.L.; methodology, A.Q. and Y.K.; software, investigation and data curation, R.C. and E.F.; formal analysis, X.Y. and Y.L.; writing—original draft preparation, Y.K. and A.Q.; writing—review and editing, Y.K. and Y.L.; funding acquisition, Y.L. and X.Y. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to express our sincere thanks to Jia Changrong for his help in the selection and setting up of experimental plots in the forestry farm.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Effects of different land-use types on the soil BG (a) and URE (b) activities. Different lowercase letters indicate that different land-use types are significantly different in the same soil layer in the same season (p < 0.05). Different capital letters indicate that different seasons are significantly different in the 0–30 cm soil layer (p < 0.05).

Figure 2. Effects of different land-use types on the soil PROT (a) and CAT (b) activities. Different lowercase letters indicate that different land-use types are significantly different in the same soil layer in the same season (p < 0.05). Different capital letters indicate that different seasons are significantly different in the 0–30 cm soil layer (p < 0.05).

Figure 3. Correlation coefficients between soil enzyme activities and other soil physical and chemical properties.

Table 1

The basic details of the sampling plots.

Land-Use Types	Stand Age(Years)	Elevation(m)	Mean Tree Height (m)	Mean DBH(cm)	Crown Density
FL	/	410	/	/	/
AL	/	380	/	/	/
PO	35	410	8.5	12.90	0.85
RP	35	370	13.5	15.61	0.86
QV	35	380	13.1	7.32	0.84

FL, farmland; AL, abandoned land; PO, Platycladus orientalis artificial forest; RP, Robinia pseudoacacia artificial forest; QV, Quercus variabilis artificial forest; DBH, diameter at breast height.

Table 2

Variance analysis of effects of land-use types, season and soil layer on soil enzymatic activity.

Source		Land-Use Types(LUT)	Season(S)	Soil Layer(SL)	LUT × S	LUT × SL	S × SL	LUT × S × SL
p	BG	0.000	0.000	0.000	0.000	0.000	0.004	0.107
	URE	0.000	0.000	0.000	0.000	0.039	0.269	0.636
	PROT	0.000	0.000	0.000	0.000	0.000	0.224	0.396
	CAT	0.000	0.000	0.012	0.015	0.337	0.006	0.408

Table 3

Multiple-regression analysis model of soil enzyme activities and other soil properties.

Equations	Adjusted R2	P
Y1 = 0.483X1 + 2.831X2 + 3.608X3 + 27.942X4 − 67.521X5 + 5.964X6 + 0.143X7 − 57.761	0.673	0.046
Y2 = 0.007X8 − 0.121X5 + 0.003X3 + 0.022X4 + 0.374	0.509	0.000
Y3 = 0.058X1 + 0.035X9 + 2.067X4 + 0.144X3 − 0.084X10 + 0.375X6 − 20.87	0.745	0.032
Y4 = 0.184X5 + 0.028X4 + 0.006X6 − 0.824	0.591	0.009

Y₁, soil BG activity; Y₂, soil URE activity; Y₃, soil PROT activity; Y₄, soil CAT activity; X₁, DOC; X₂, AK; X₃, AN; X₄, water content; X₅, pH; X₆, NO₃⁻-N content; X₇, SOC; X₈, silt volume fractions; X₉, MBC; X₁₀, MBN.

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