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Article

Kinetic Parameters of Soil Enzymes and Temperature Sensitivity Under Different Mulching Practices in Apple Orchards

by
Yaokun Jiang
1,2,3,
Huike Li
4,
Meng Liang
1,2,3,
Yang Wu
4,5,
Ziwen Zhao
4,5,
Yuanze Li
4,5,
Guobin Liu
1,2,3,* and
Sha Xue
1,2,3,4
1
The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling 712100, China
2
Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling 712100, China
5
State Key Laboratory of Soil and Water Conservation and Desertification Control, Northwest A&F University, Yangling 712100, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(3), 617; https://doi.org/10.3390/agronomy15030617
Submission received: 20 January 2025 / Revised: 8 February 2025 / Accepted: 25 February 2025 / Published: 28 February 2025
(This article belongs to the Section Innovative Cropping Systems)
Browse Figures
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Abstract

:
Soil mulching practices in apple orchards offer an effective solution to combat declining soil quality, restore land productivity, and boost apple yield. The kinetic parameters of soil enzymes, specifically the maximum reaction rate (Vmax) and the Michaelis constant (Km), are critical indicators of enzyme activity, while the temperature sensitivity (Q10) reflects the thermal stability of the enzymatic reaction system. However, the effects of different mulching practices on soil enzyme kinetic parameters and their temperature sensitivity remain poorly understood, and there is no consensus regarding the most effective mulching strategies for soil conservation. To address this gap, we focused on a typical apple orchard ecosystem in the Loess Plateau region and investigated the responses of soil enzyme kinetic parameters and their temperature sensitivity to various mulching practices, including different cover materials, grass species for cover crops, and cover duration. Our results show that, among the mulching practices, both ryegrass (RE) and maize straw significantly enhanced the maximum enzyme catalytic reaction rates (Vmax) and catalytic efficiency (Kcat) of β-glucosidase (BG), N-acetyl-β-glucosaminidase (NAG), and alkaline phosphatase (ALP). In contrast, black fabric (BF) reduced the temperature sensitivity of the enzyme system by decreasing Vmax and Kcat. Among the grass species used for cover, crown vetch (CV) had the most pronounced effect on Vmax, while long-term grass cover was more effective in improving the nutrient utilisation capacity of the soil enzyme system. Overall, maize straw and long-term grass cover were found to be the most effective in enhancing the soil enzyme system’s ability to decompose and utilise substrates efficiently. This study identifies soil nutrients as key factors influencing the temperature sensitivity of enzyme kinetics. Our findings provide a scientific basis for developing and applying orchard conservation practices and offer technical support for selecting and promoting soil management strategies that improve soil quality and contribute to the sustainable development of the apple industry in the Loess Plateau.

1. Introduction

China possesses a minimum of 2.22 million hectares dedicated to apple cultivation, representing approximately 50% of the global apple cultivation area [1]. The Loess Plateau stands out as a significant region within China for apple farming, with the apple industry becoming a major economic crop in this region [2]. High-quality apple production in the Loess Plateau is attributed to its high sunlight, significant day–night temperature differences, and deep soil [3]. However, the apple industry in this region has been constrained by adverse factors. Approximately 80% of the apple orchards in this region rely on rain-fed (dryland) cultivation and have poor soil fertility, characterised by low soil nitrogen and organic matter content. The presence of loess parent material combined with irregular precipitation (60% occurring between July and September) leads to severe nutrient loss and a decline in the fertility of orchard soils in the region [4,5].
To combat soil fertility degradation and improve soil quality, protective planting technologies involving mulching practices have gained popularity over the past decade [4,6,7,8]. Protective mulch planting techniques usually refer to agricultural ground management techniques that utilise organic or inorganic substances to mulch the surfaces of agricultural soils (farmland, orchards, and vegetable plots) [6,8,9,10]. Orchard protective soil management measures can improve the soil environment, which in turn affects the composition, growth, metabolism, and functional expression of soil microorganisms, and promotes microorganisms to develop in the direction conducive to the improvement in soil quality in the orchard [4,11]. This, in turn, boosts increasing yields, quality, and efficiency, achieving a balance of economic, social, and ecological benefits [7,12].
Soil enzymes have significant functions in the cycling of soil nutrients and the breakdown of organic matter and their dynamics are intricately linked to the productivity of soil ecosystems and the turnover of nutrients [13]. Soil enzyme kinetic parameters and their thermodynamic characteristics serve as important indicators of soil quality and health, which can be used to visualise the stability of soil function [14,15,16]. The kinetic characteristics of soil enzymes are determined by parameters such as the maximum reaction rate (Vmax) and the half-saturation constant (Km) [17]. When analysing the kinetic properties of soil enzymes, the ratio of Vmax to Km is frequently employed to reflect the intensity and efficiency of the catalytic reaction of soil enzymes, expressed as Kcat [18,19]. In recent years, an increasing number of researchers have discussed the temperature sensitivity of enzyme dynamics using enzyme kinetic parameters to characterise the response of the enzyme system to changes in the external environment, thus reflecting the thermal stability of the enzyme system in the soil ecosystem. Enzyme systems with lower Km values and lower temperature sensitivity are crucial for maintaining the nutrient cycling function of ecosystems [20,21].
Existing research has shown that judicious application of orchard mulching measures can enhance tree growth, increase fruit yield, and enrich fruit quality by improving soil moisture and nutrient conditions [1,4,22,23]. Moreover, soil enzyme activity can quickly respond to changes in ground cover measures [24]. There is a limited amount of research on the variations in soil enzyme kinetic parameters and their thermodynamic characteristics under different mulching treatments, leaving the mechanisms affecting soil ecological stability in response to these treatments largely unexplored [25,26]. This limits our understanding of protective cultivation methods in agricultural ecosystems. To ensure sustainable agricultural production, it is essential to uphold high soil quality and promote a healthy environment. Transforming the original information on soil enzyme systems under protective cultivation into an understanding of the soil functions on which agricultural ecosystems depend is valuable.
This study analysed the soil enzyme kinetic parameters. Their temperature sensitivities were in response to various mulching practices, grass mulching durations, and grass species in the Loess Plateau region. Using soil enzyme kinetic parameters and their temperature sensitivity as indicators of the soil response function, orchard mulching patterns were optimised to improve agricultural productivity by assessing the ecological benefits of conservation tillage methods and identifying the most effective soil and water mulching methods. Specifically, we analysed the effects of different mulching measures, including grass cover (e.g., white clover, orchard grass, and small crown flower), as well as the duration of grass cover and other ground cover materials, such as ryegrass, cornstalk, and black ground fabric. This study makes the following assumptions: (1) grass and straw mulching would enhance the soil enzyme system’s ability to decompose substrates and efficiently utilise them; (2) grass cover of different species would improve the catalytic efficiency of soil enzymes, with longer durations of grass cover having a more pronounced effect on nutrient utilisation by the soil enzyme system; and (3) mulching practices would affect the temperature sensitivity of soil enzyme kinetics by modifying soil nutrient content, thereby influencing nutrient cycling and functional stability within the soil ecosystem.

2. Materials and Methods

2.1. Experimental Design

The study was conducted in Baishui County, Weinan City, Shaanxi Province, at the Baishui Apple Experimental Station of Northwest A&F University (109°56′ E, 35°21′ N; average elevation 850 m). The soil was predominantly loess, consisting of 87% sandy loam and 8% clay. The study area is significantly influenced by complex terrain, resulting in large climatic differences within the region. The average annual total solar radiation is 128.13 kcal/cm2·year, with abundant sunshine (2480 h) and a frost-free period of 207 days, which is favourable for producing high-quality apples. The average annual temperature is 11.4 °C, with an average annual precipitation of 577.8 mm, which is unevenly distributed with considerable variability; approximately 60% of the rainfall occurs in the summer months (July to September). The annual moisture index is 0.50. Baishui County has a warm temperate continental monsoon climate, characterised by cold, long winters with dry and windy conditions; rapid warming and dryness in spring with frequent cold air activity; hot, humid summers with frequent showers; and rapid cooling with damp, cloudy weather in autumn. The experimental material used was Fuji apples (Malus pumila cv. Fuji) planted in 2006, spaced at 1 m intervals with 4 m row spacing. Local plant species commonly used for grass cover were selected for the experimental treatments [8,11].
The experiment adopted a randomised complete block design with eight mulching measures. Each cover measurement included three replicate plots, resulting in 24 plots, each measuring 48 m2. Each plot consisted of twelve apple trees arranged in two rows. Orchard management practices were consistent throughout the experiment.
The experimental design included three treatments (Table 1):
(1) Different mulching practices were established in 2012. Conventional tillage control (CK): inter-row planting of perennial ryegrass (Lolium perenne Linn.) (RE) with a planting width of 3 m, inter-row coverage with maize straw (CS) with a coverage width of 4 m and a thickness of approximately 15 cm, and coverage with horticultural black fabric (BF) on both sides of the apple trees with 1 mm thickness and a width of 1 m on each side.
(2) Different grass mulching measures were established in 2006. Inter-row planting of perennial crown vetch (Coronilla varia Linn.) (CV): inter-row planting of perennial orchard grass (Dactylis glomerate Linn.) (OG): inter-row planting of perennial white clover (Trifolium repens Linn) (WC).
(3) Different durations of grass mulching. Inter-row planting of white clover in 2006 and 2015 (15aWC; 6aWC). Conventional Tillage Control (CK). During the experiment, regular weeding and removal of plant residues were performed every month to ensure that no plant residues were present on the experimental field surface.

2.2. Sampling Method

Soil samples were gathered employing a five-point sampling technique. Each zone yielded five soil cores (with a diameter of 5 cm and depth of 20 cm), extracted randomly using an open-barrel probe. These cores were crushed and thoroughly mixed to create a composite soil sample, a process repeated for each zone. Following this sampling method, two soil samples were obtained from each zone, resulting in 48 soil samples (24 areas × two replicates). Any weeds, roots, or gravel were manually eliminated from these samples, which were then sieved through a 2 mm mesh screen and divided equally into two parts. The first portion underwent physicochemical analyses, while the second part was refrigerated at 4 °C for subsequent measurements of microbial biomass carbon, nitrogen, phosphorus, and enzyme activity [8,9,27].
Soil pH (pH): the extract is prepared with a water ratio of 2.5:1 and measured with a pH meter. Soil moisture content was determined by the alcohol combustion method. Soil nutrient indicators include soil available nutrient content (NH4+, NO3−, and AP) and total soil nutrient content (TN and TP). Soil organic carbon (SOC) was measured using the H2SO4–K2Cr2O7 oxidation method. Total nitrogen (TN) content in the soil was determined by the Kjeldahl method. Total phosphorus (TP) content was measured using the molybdenum blue colourimetric method at 700 nm wavelength after digestion with H2SO4 and HClO4. Soil ammonium (NH4+) and nitrate (NO3−) were measured using a flow analyser (AutAnalyel, Bran + Luebbe GmbH, Norderstedt, Germany). Soil-available phosphorus (AP) content was extracted using a 0.5 mol/L NaHCO3 solution at pH 8.5 and measured by the molybdenum blue colourimetric method. The soil chemical properties are listed in detail in Table 2 and Table 3.

2.3. Soil Incubation and Enzyme Assays

For the soil enzyme assay, soil samples were incubated at four temperatures (5 °C, 15 °C, 25 °C, and 35 °C). A total of 40 g of the soil sample was weighed for each treatment and placed in an incubator at 25 °C for 7 days of pre-incubation. Following pre-incubation, the soil was further incubated in each of the four temperature-controlled chambers for thirty days. During this period, the soil’s moisture content was maintained at 60% of the field holding capacity through weighing, and the bottles were periodically opened for aeration. Finally, the soil samples were stored at −20 °C for testing [13,14].
Fluorimetric microplate assays were used to measure the kinetics of the soil enzymes involved in the C, N, and P cycles. The enzyme activity of β-glucosidase (BG), N-acetyl-β-glucosidase (NAG), and alkaline phosphatase (ALP) were measured with seven substrate concentration gradients (50, 100, 200, 300, 400, 500, and 600 μ mol L−1). A microwell multifunction microplate reader was used for excitation at 365 nm and fluorescence detection at 450 nm. First, 3 g of fresh soil sample was weighed on a crystal plate, and 125 mL of Tris buffer was added. The buffer pH was adjusted with hydrochloric acid or sodium hydroxide to the pH of the soil samples. Then, 150 μL of soil suspension and 50 μL of test enzyme substrate were sequentially added to the 96 micro-well micro-plate and the control micro-wells were set. The microplate was then placed in a constant-temperature incubator at the respective culture temperatures. ALP was incubated for 0.5 h, BG for 2 h, and NAG for 4 h before measurement under dark and light-proof conditions [28].
The effects of different coverage measures on the kinetic parameters of soil enzymes were studied, and each parameter was derived from the Michaelis–Menten equation, as follows [29]:
V = Vmax [S]/(Km + [S])
where V is the activity of the reaction enzyme, Vmax is the maximum activity of the reaction enzyme (μmol h−1 g−1), S is the substrate concentration, and Km is the half-saturation constant (μmol L−1).
The formula for calculating the Q10 of soil enzyme activity is as follows [30]:
Q10 = eSlope×10
where the slope is the logarithmic transformation of soil enzyme activity, and the slope of the linear regression equation was established with the culture temperature.

2.4. Data Analysis

The data were numerically collated using Microsoft Excel 2019 and subjected to statistical analysis using SPSS 25.0 (https://www.ibm.com/products/spss-statistics, accessed on 5 July 2024, Chicago, IL, USA). Data processing and analysis were conducted using SPSS 25.0 (https://www.ibm.com/products/spss-statistics, accessed on 5 July 2024, Chicago, IL, USA) and R (version 4.1.3; http://www.R-project.org, accessed on 7 July 2024). We employed single-factor (one-way ANOVA), Duncan’s method of variance analysis, and multiple comparisons (p < 0.05). Using Canoco 5.0 (http://canoco5.com/, accessed on 8 July 2024), redundancy analysis (RDA) plots were performed with Origin2021 (OriginLab, Northampton, MA, USA).

3. Results

3.1. Effect of Mulching Practices on the Kinetic Parameters of Soil Enzymes Under Temperature Change

With temperature variation, the kinetic parameters of different enzymes exhibited diverse responses to various mulching measures (Figure 1 and Figure 2). As the temperature changed, ALP-Vmax initially increased and then decreased with rising temperatures, reaching its peak at 25 °C. In contrast, both BG and NAG showed increasing trends in Vmax with increasing temperature. In general, the Km tended to decrease with increasing temperature for all enzymes. The Kcat for all three enzymes under different conditions increased with increasing temperature.
Specifically, under different types of mulching practices, at an optimal temperature of 25 °C for enzymatic reactions, the Vmax trends of the three enzymes were generally consistent, with the order of Vmax values being CS > RE > BF. CS significantly increased the Vmax25 of enzymes compared to CK (p < 0.05), whereas BF significantly decreased the Vmax25 of enzymes (p < 0.05). At 25 °C, among different types of mulching measures, CS increased the Kcat of enzymes compared to CK, while BF significantly reduced the Kcat of enzymes (p < 0.05) (Figure 3, Figure 4 and Figure 5).
The Vmax of the enzyme changed with temperature in different species, with all sizes being CV > OG > WC at 25 °C. Specifically, the Vmax25 values for ALP and BG under the CV treatment were significantly higher than those under CK (p < 0.05). Among the different grass cover species treatments, the Kcat of the three enzymes at 25 °C is generally increased compared to CK.
As grass cover duration increased, the Vmax of the soil enzymes showed an upward trend at various temperatures. Under 25 °C conditions, 15 aWC significantly enhanced the Vmax of ALP and BG compared to CK (p < 0.05). At different temperatures, compared with CK, short-term grass cover (6a WC) led to a decrease in the Kcat of the three enzymes, whereas long-term grass cover (15a WC) resulted in an increase in the Kcat of the three enzymes.

3.2. Thermodynamic Characteristics of Soil Enzyme Kinetic Parameters and Their Influencing Factors

Mulching measures influenced the temperature sensitivity of Vmax and Km, which are characterised by the temperature sensitivity of the maximum enzyme reaction rate (Vmax-Q10) and the temperature sensitivity of the half-saturation constant (Km-Q10), respectively. Under different mulching practices, the Vmax-Q10 of the three enzymes increased compared to that of the CK, in the order of CS > RE > BF. Specifically, RE and CS significantly increased the Vmax-Q10 of the BG compared to that of the CK (p < 0.05) (Figure 6). All three grass cover types increased the Vmax-Q10 of the three enzymes compared with that of the CK, in the order of CV > OG > WC. The Vmax-Q10 values of the three enzymes generally increased with grass cover duration. The temperature sensitivity of Km in response to mulching was consistent with Vmax overall. Different types of mulching practices significantly increased the Km-Q10 of ALP, and the CV cover treatment significantly increased the Km-Q10 of ALP and BG compared with CK.
Redundancy analysis (Figure 7) indicated that environmental variables effectively explained the variation in the Q10 of soil enzyme kinetic parameters, with a total explanatory rate of 93.5%. The correlation between Q10 and soil nutrients was strong, and SOC, MBP, NH4+ -N, and MBN had a significant effect on the Q10 of the soil enzyme kinetic parameters (p < 0.05).

4. Discussion

4.1. Response of Soil Enzyme Kinetic Parameters to Mulching Practices

Different mulching practices influence enzymatic characteristics by shaping unique soil microenvironments. Compared with the control group, mulching measures significantly increased the Vmax of the enzymes (p < 0.05). Several factors contribute to this phenomenon. First, continuous input of organic matter under straw and ryegrass cover stimulated microbial functional expression, promoting the accumulation of organic matter in the soil, leading to an increase in available substrates for soil enzymes and an enhancement of enzyme activity. Second, BG participates in the breakdown of complex molecules found in plant residues, such as cellulose and hemicellulose [7,14]. Grass species, such as ryegrass, typically have a high carbon-to-nitrogen ratio and are rich in cellulose and hemicellulose. This results in elevated levels of BG substrate within the soil beneath their coverage, prompting microorganisms to enhance enzyme production, consequently raising Vmax [31]. Thirdly, the NAG enzyme boosts nitrogen levels by breaking down chitin found in fungal cell walls [11]. Straw and ryegrass cover resulted in the substantial presence of plant residues on the soil surface, increasing fungal abundance in the soil. The increase in the potential maximum reaction rate of NAG was closely related to this phenomenon [32]. In comparison to the control group, horticultural cover negatively affected the Vmax and Kcat of soil enzymes, likely because polyethylene releases toxic additives, leading to a reduction in enzyme activity in the soil [33].
Different types of grass cover primarily influence enzymatic characteristics by affecting soil nutrient content. Previous studies have indicated that soil microbial activity in the Loess Plateau is constrained by low phosphorus bioavailability and the accumulation of soil carbon and nitrogen [1,17]. Our results revealed that, compared to CK, the Vmax of ALP significantly increased under herbaceous plant cover. Redundancy analysis showed a notable positive correlation between the ALP enzymatic parameters and soil organic carbon content. Herbaceous plant cover increases the presence of plant roots in orchard soil, leading to an increase in soil carbon content and restricting soil phosphorus availability to the microbial community. This may result in increased catalysis of alkaline phosphatase by the microbial community to sustain its biomass and metabolic growth [16,34]. What is more, previous research has confirmed that leguminous plants, serving as cover crops, can increase the nitrogen content of the soil [35]. White clover, a commonly used leguminous plant, can increase the soil nitrogen content owing to its plant characteristics. Simultaneously, its lower carbon-to-nitrogen ratio makes it more easily decomposable, rapidly increasing soil organic matter content.
Long-term grass cover proved more beneficial than short-term grass cover in enhancing soil functionality. Ecosystem changes are gradual and continuous processes, and the functional diversity and community building of microorganisms cannot be fully expressed in the short term [8,11]. The results of our study confirmed this hypothesis. In comparison to the control group, the Vmax of soil enzymes significantly increased under 15-year white clover cover (15 aWC) (p < 0.05), indicating that long-term herbaceous plant cover is more effective in activating soil enzymes than short-term herbaceous plant mulching measures. The response of the three enzymes to grass mulching (Figure 3, Figure 4 and Figure 5) suggests that long-term grass cover crop treatments are conducive to improving the catalytic ability of soil enzymes and enhancing the enzyme utilisation efficiency of the substrates. This is likely due to long-term grass cover providing more organic matter and root exudates, thereby activating the soil enzyme system [26].

4.2. Response of Soil Enzymes Under Coverage Treatment to Temperature Changes

In this study, the increase in Kcat with rising temperature was primarily attributed to an increase in Vmax. This is consistent with previous findings, which show that Kcat increases significantly above 20 °C [10]. The increase in Kcat suggests that higher temperatures stimulate microbial processes, enhancing enzyme production. Interestingly, Km did not show significant changes, which indicates that enzymes may adapt to the increased temperature by optimising their catalytic capacity [36]. The stability of Km in the face of rising temperatures suggests that the soil microbial system can adjust its enzyme characteristics to maintain efficient catalysis. This adaptation is important for understanding how soil microbial communities respond to temperature-induced changes in enzyme kinetics.
The minimal variation in Km observed in this study indicates that soil enzymes maintain stability despite temperature fluctuations. This stability can be explained by the expression of enzyme isoforms with varying optimal temperature ranges, allowing the enzyme system to function effectively across a wide range of environmental conditions [13,37]. Additionally, the low flexibility of a single enzyme type, which can catalyse reactions over a broad temperature range, may also contribute to the observed static Km values. This stability is crucial for maintaining efficient catalytic reactions in response to changing environmental conditions. The relationship between enzyme isoform expression and temperature adaptation is an important factor in the resilience of soil ecosystems to temperature fluctuations.
The increase in Kcat under grass cover treatments suggests that cover crops can improve soil microbial and enzymatic responses to temperature fluctuations. By providing additional organic matter and promoting microbial activity, cover crops help maintain enzyme stability and increase their efficiency. This is particularly important under changing environmental conditions, supporting the hypothesis that mulching practices and cover crops can buffer the impacts of temperature changes on soil enzymes. The improved thermal stability observed in this study suggests that cover crops can alter soil microenvironments and microbial communities, ultimately enhancing the soil’s enzymatic responses to temperature changes [21,38].

4.3. Temperature Sensitivity of Soil Enzyme Kinetics in Response to Mulching Practices

The thermodynamic characteristics of soil enzyme kinetics offer valuable insights into the thermal stability of soil enzymes across various mulching practices. According to the Arrhenius equation, a lower temperature sensitivity generally implies better thermal stability of the enzyme system [21]. While many studies have indicated that warming decreases the Q10 values of enzymes [15,39,40], this is inconsistent with our findings. The change in microbial physiology may offset the deviation from the Arrhenius law because of the accelerated enzyme reaction rates with warming to some extent. From physiological and evolutionary perspectives, natural selection in microbes should produce adaptations to respond to their local environment. This process favours the selection of enzyme systems best suited to withstand temperature stress [41]. Therefore, the effects of mulching practices on the soil microbial physiology may maintain the thermal stability of the soil enzyme system [9,36,42].
The findings from this research indicated that the Vmax-Q10 of the three enzymes under different mulching practices was higher than that of the control group. This result might be due to several factors. First, straw cover and grass mulching practices alter the soil environment of the orchard, changing microbial growth strategies; this alteration prompts adjustments in the composition of soil enzyme systems, including their isoforms, and their responsiveness to temperature variations. Second, grass and straw cover introduce nutrients into the soil, causing changes in the functional genes of the soil microbial community and carbon cycling [43,44]. Changes in the soil environment exacerbate the effect of temperature on the potential activity of β-glucosidase and alter the thermal stability of the enzyme system. Lastly, temperature sensitivity changes in NAG enzyme kinetics can be attributed to changes in microbial community abundance and functionality caused by N enrichment due to straw cover [45,46].
The improved thermal stability of the enzyme system under white clover cover can be attributed to several mechanisms. White clover alters the soil microenvironment by enhancing moisture retention and increasing organic matter input, moderating temperature fluctuations. This promotes a stable microbial community that supports a more diverse enzyme system, with isoenzymes that function well under a range of temperature conditions [36]. In particular, the nutrients provided by white clover and the increased microbial biomass may help maintain enzyme stability under high-temperature stress. Furthermore, by lowering Vmax and increasing Km, white clover cover helps maintain a balance between enzyme efficiency and stability, ensuring that enzymes can function effectively despite temperature fluctuations. This response highlights the importance of selecting appropriate cover crops to manage soil temperature and improve enzyme stability, especially under warming conditions [3,11,27].
The increase in Vmax-Q10 with longer grass production suggests that long-term mulching practices, such as the use of cover crops, promote sustained microbial activity and enzymatic efficiency. Organic matter input from grass cover stimulates microbial populations, leading to an increase in enzyme secretion. This is reflected in the enhanced catalytic capacity (Vmax) of the enzymes over time, as microbes adapt to the steady availability of nutrients and substrates. Additionally, the long-term presence of cover crops may improve soil structure and nutrient cycling, further supporting microbial health and enzyme function. These findings emphasise the importance of long-term mulching practices in fostering a stable and resilient enzyme system that can better respond to temperature changes.

5. Conclusions

This study demonstrated that in apple orchards on the Loess Plateau, various mulching practices, grass species, and varying durations of grass cover enhance the activity and catalytic efficiency of the soil enzyme system, influencing its thermal stability. Specifically, both corn straw cover and grass cover effectively boost the soil enzyme system’s ability to efficiently decompose and utilise substrates. Long-term grass cover proves more effective in improving the capability of the soil enzyme system to utilise nutrients, whereas ground cloth cover and short-term grass cover contribute to the stability of the enzyme system. By examining soil enzyme dynamics across different mulching methods and temperature trends, this study sheds light on soil enzyme temperature sensitivity, providing a theoretical basis for orchard ground management strategies, soil quality assessment, and ecological development. It is recommended to implement long-term grass cover for improved soil enzyme activity and nutrient utilisation. Future studies should focus on the long-term effects of different mulching practices on soil health and explore the combined influence of mulching and climate factors.

Author Contributions

Methodology, Y.W. and Y.L.; Formal analysis, Z.Z.; Writing—original draft, Y.J.; Writing—review & editing, S.X.; Visualization, M.L.; Supervision, H.L.; Funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Natural ScienceFoundation of China (42171301) and the Shaanxi Creative TalentsPromotion Plan-Technological Innovation Team (2023-CX-TD-37).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Vmax characteristics of soil enzymes under different coverage measures. Note: Vmax: the maximum enzymatic reaction rate; BG: β-glucosidase; NAG: N-acetyl-β-glucosidase; ALP: alkaline phosphatase; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch. Different letters indicate a significant difference (p < 0.05).
Figure 1. Vmax characteristics of soil enzymes under different coverage measures. Note: Vmax: the maximum enzymatic reaction rate; BG: β-glucosidase; NAG: N-acetyl-β-glucosidase; ALP: alkaline phosphatase; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch. Different letters indicate a significant difference (p < 0.05).
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Figure 2. Km characteristics of soil enzymes under different coverage measures. Note: Km: the half-saturation constant; BG: β-glucosidase; NAG: N-acetyl-β -glucosidase; ALP: alkaline phosphatase; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch. Different letters indicate a significant difference (p < 0.05).
Figure 2. Km characteristics of soil enzymes under different coverage measures. Note: Km: the half-saturation constant; BG: β-glucosidase; NAG: N-acetyl-β -glucosidase; ALP: alkaline phosphatase; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch. Different letters indicate a significant difference (p < 0.05).
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Figure 3. Effect of ground cover measures on ALP catalytic efficiency. Note: Kcat: catalytic efficiency; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
Figure 3. Effect of ground cover measures on ALP catalytic efficiency. Note: Kcat: catalytic efficiency; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
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Figure 4. Effect of ground cover measures on BG catalytic efficiency. Note: Kcat: catalytic efficiency; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
Figure 4. Effect of ground cover measures on BG catalytic efficiency. Note: Kcat: catalytic efficiency; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
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Figure 5. Effect of ground cover measures on NAG catalytic efficiency. Note: Kcat: catalytic efficiency; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
Figure 5. Effect of ground cover measures on NAG catalytic efficiency. Note: Kcat: catalytic efficiency; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
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Figure 6. Temperature sensitivity of enzyme kinetic parameters for different coverage measures. Note: CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; Vmax-Q10: temperature sensitivity of kinetic parameters of Vmax; Km-Q10: temperature sensitivity of kinetic parameters of Km. Different lowercase letters indicate significant differences among treatments (p < 0.05); different uppercase letters indicate significant differences among species coverage (p < 0.05).
Figure 6. Temperature sensitivity of enzyme kinetic parameters for different coverage measures. Note: CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; Vmax-Q10: temperature sensitivity of kinetic parameters of Vmax; Km-Q10: temperature sensitivity of kinetic parameters of Km. Different lowercase letters indicate significant differences among treatments (p < 0.05); different uppercase letters indicate significant differences among species coverage (p < 0.05).
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Figure 7. Redundancy analysis of temperature sensitivity Q10 with environmental factors. Note: SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; AP: soil available phosphorus; NO3−-N: soil nitrate nitrogen; NH4+-N: soil ammonium nitrogen; WOC: water-soluble organic carbon; WC: water content; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; MBP: microbial biomass phosphorus.
Figure 7. Redundancy analysis of temperature sensitivity Q10 with environmental factors. Note: SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; AP: soil available phosphorus; NO3−-N: soil nitrate nitrogen; NH4+-N: soil ammonium nitrogen; WOC: water-soluble organic carbon; WC: water content; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; MBP: microbial biomass phosphorus.
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Table 1. The different ground mulching measures of apple orchard.
Table 1. The different ground mulching measures of apple orchard.
Test ProcessingMulching PracticesLogogramNumber of YearsGround Cover Material
Different years of
grass mulching		Clear tillage controlCK15 yearsClearing cover
6a white clover mulch6a WC6 yearsWhite clover (Trifolium repens Linn.)
15a white clover mulch15a WC15 yearsWhite clover (Trifolium repens Linn.)
Different grass species of grass mulchingClear tillage controlCK15 yearsClearing cover
15a white clover mulch15a WC15 yearsWhite clover (Trifolium repens Linn.)
15a orchard grass mulchOG15 yearsOrchard grass (Dactylis glomerate Linn.)
15a small crown flower mulchCV15 yearsSmall crown flower (Coronilla varia Linn.)
Different kinds of ground cover measuresClear tillage controlCK15 yearsClearing cover
9a ryegrass mulchRE9 yearsRyegrass (Lolium perenne Linn.)
9a cornstalk mulchCS9 yearsCorn stalk
9a black ground fabric mulchBF9 yearsPolyethylene black floor cloth (1 mm)
Table 2. Soil chemical properties under different ground cover measures.
Table 2. Soil chemical properties under different ground cover measures.
Test ProcessingMulching PracticespHSOC (g∙kg−1)TN (g∙kg−1)TP (g∙kg−1)AP (mg∙kg−1)NO3−-N (mg∙kg−1)NH4+-N (mg∙kg−1)WC (%)
Different years of grass mulchingCK8.40 ± 0.12 a13.88 ± 0.91 c1.11 ± 0.13 b0.81 ± 0.07 b10.80 ± 2.28 b8.95 ± 6.14 a2.53 ± 0.28 b0.14 ± 0.01 a
6a WC8.37 ± 0.06 a18.23 ± 0.64 b c1.13 ± 0.08 b1.42 ± 0.10 a34.83 ± 1.93 a23.87 ± 8.61 a5.49 ± 1.29 a0.15 ± 0.00 a
15a WC8.32 ± 0.03 a23.14 ± 1.89 a1.51 ± 0.11 a0.82 ± 0.09 b14.27 ± 2.81 b21.28 ± 6.40 a6.48 ± 0.73 a0.14 ± 0.01 a
Different grass species of grass mulchingCK8.40 ± 0.12 a13.88 ± 0.91 c1.11 ± 0.13 c0.81 ± 0.07 a10.80 ± 2.28 a8.95 ± 6.14 a2.53 ± 0.28 c0.14 ± 0.01 a
15a WC8.32 ± 0.03 a23.14 ± 1.89 b1.51 ± 0.11 b0.82 ± 0.09 a14.27 ± 2.81 a21.28 ± 6.40 a6.48 ± 0.73 a0.14 ± 0.01 a
OG8.40 ± 0.09 a23.31 ± 1.53 b1.24 ± 0.07 c0.92 ± 0.08 a9.63 ± 1.07 a20.38 ± 21.98 a3.72 ± 0.53 b0.14 ± 0.01 a
CV8.26 ± 0.13 b34.36 ± 3.74 a2.04 ± 0.18 a0.91 ± 0.05 a9.95 ± 2.38 a27.36 ± 7.92 a4.12 ± 0.80 b0.14 ± 0.02 a
Different kinds of ground cover measuresCK8.40 ± 0.12 a13.88 ± 0.91d1.11 ± 0.13 b0.81 ± 0.07 c10.80 ± 2.28 c8.95 ± 6.14 b2.53 ± 0.28 b0.14 ± 0.01 b
RE8.30 ± 0.03 a22.57 ± 1.60 c1.25 ± 0.10 b1.02 ± 0.14 c20.09 ± 3.54 c15.00 ± 2.61 b4.63 ± 1.23 a0.15 ± 0.01 b
CS7.79 ± 0.08 b40.46 ± 3.95 a2.13 ± 0.37 a3.00 ± 0.61 b95.64 ± 7.52 b41.54 ± 4.03 a3.88 ± 0.57 a b0.22 ± 0.02 a
BF7.66 ± 0.05 c36.54 ± 2.09 a b2.00 ± 0.13 a3.59 ± 0.35 a74.30 ± 16.27 a58.46 ± 11.71 a3.79 ± 0.64 a b0.21 ± 0.01 a
Note: SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; AP: soil available phosphorus; NO3−-N: soil nitrate nitrogen; NH4+-N: soil ammonium nitrogen; WC: water content; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
Table 3. Soil microbial biomass under different ground cover measures.
Table 3. Soil microbial biomass under different ground cover measures.
Test ProcessingMulching PracticesMBC (mg∙kg−1)MBN (mg∙kg−1)MBP (mg∙kg−1)
Different years of grass mulchingCK148.55 ± 6.78 b10.42 ± 0.87 a3.57 ± 0.16 b
6a WC255.27 ± 9.43 a9.76 ± 2.86 a4.31 ± 0.81 b
15a WC142.21 ± 6.59 b9.48 ± 1.36 a6.23 ± 2.01 a
Different grass species of grass mulchingCK148.55 ± 6.78 c10.42 ± 0.87 b3.57 ± 0.16 c
15a WC142.21 ± 6.59 c9.48 ± 1.36 b6.23 ± 2.01 b
OG396.21 ± 8.18 a10.68 ± 0.30 b6.84 ± 0.39 b
CV245.67 ± 7.58 b15.75 ± 1.08 a9.53 ± 1.19 a
Different kinds of ground cover measuresCK148.55 ± 6.78 c10.42 ± 0.87 a3.57 ± 0.16 c
RE314.92 ± 6.91 a5.43 ± 1.74 c6.17 ± 1.14 c
CS315.10 ± 8.56 a8.23 ± 0.48 b13.24 ± 1.95 b
BF183.37 ± 3.71 b6.64 ± 2.06 b c21.52 ± 4.19 a
Note: MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; MBP: microbial biomass phosphorus; CK: clear tillage control; RE: 9a ryegrass mulch; CS: 9a cornstalk mulch; BF: 9a black ground fabric mulch; 6a WC: 6a white clover mulch; 15a WC: 15a white clover mulch; OG: 15a orchard grass mulch; CV: 15a small crown flower mulch; different letters indicate a significant difference (p < 0.05).
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Jiang, Y.; Li, H.; Liang, M.; Wu, Y.; Zhao, Z.; Li, Y.; Liu, G.; Xue, S. Kinetic Parameters of Soil Enzymes and Temperature Sensitivity Under Different Mulching Practices in Apple Orchards. Agronomy 2025, 15, 617. https://doi.org/10.3390/agronomy15030617

AMA Style

Jiang Y, Li H, Liang M, Wu Y, Zhao Z, Li Y, Liu G, Xue S. Kinetic Parameters of Soil Enzymes and Temperature Sensitivity Under Different Mulching Practices in Apple Orchards. Agronomy. 2025; 15(3):617. https://doi.org/10.3390/agronomy15030617

Chicago/Turabian Style

Jiang, Yaokun, Huike Li, Meng Liang, Yang Wu, Ziwen Zhao, Yuanze Li, Guobin Liu, and Sha Xue. 2025. "Kinetic Parameters of Soil Enzymes and Temperature Sensitivity Under Different Mulching Practices in Apple Orchards" Agronomy 15, no. 3: 617. https://doi.org/10.3390/agronomy15030617

APA Style

Jiang, Y., Li, H., Liang, M., Wu, Y., Zhao, Z., Li, Y., Liu, G., & Xue, S. (2025). Kinetic Parameters of Soil Enzymes and Temperature Sensitivity Under Different Mulching Practices in Apple Orchards. Agronomy, 15(3), 617. https://doi.org/10.3390/agronomy15030617

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