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Article

Effect of Nitrogen and Phosphorus on Soil Enzyme Activities and Organic Carbon Stability in Qinghai–Tibet Plateau

by
Jiaying Zhai
1,2,3,†,
Huakun Zhou
4,5,†,
Yang Wu
6,†,
Guoliang Wang
2,3,6,* and
Sha Xue
2,3,6,*
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
Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Regions, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810000, China
5
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810000, China
6
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(7), 1376; https://doi.org/10.3390/agronomy14071376
Submission received: 21 May 2024 / Revised: 20 June 2024 / Accepted: 24 June 2024 / Published: 26 June 2024
(This article belongs to the Section Grassland and Pasture Science)
Browse Figures
Versions Notes

Abstract

:
Alpine grassland ecosystems are the most important ecosystem type, exhibiting a high sensitivity to anthropogenic nitrogen (N) and phosphorus (P) inputs into terrestrial ecosystems and significantly affecting the carbon (C) cycle within these ecosystems. However, the effects of N and P additions on soil C stability and the processes of organic C transformation remain unclear. This study measured the soil enzyme activities and oxidizable C fractions over a 9-year period following N and P additions to an alpine meadow in the Qinghai–Tibet Plateau. It included four treatments: control (CK), N addition, P addition, and combined N and P addition (NP), utilizing statistical methods such as analysis of variance (ANOVA), correlation analysis, and redundancy analysis. The findings indicated that NP addition significantly increased the non-labile soil oxidizable C fraction in the topsoil layer. Redundancy and correlation analyses revealed strong associations between the vegetation characteristics, C-cycling enzyme activities, soil-oxidized C fractions, and SOC stability index. These results underscore the role of NP addition in enhancing SOC accumulation and stability in grassland ecosystems, with the soil vegetation properties and C source enzyme activities serving as key regulators of the SOC stability. This study offers valuable insights into predicting the SOC dynamics amid rising N and P availability, thereby elucidating the effect of nutrient addition on soil C-cycling mechanisms.

1. Introduction

The atmospheric deposition of nitrogen (N) and phosphorus (P) has increased significantly due to anthropogenic activities [1,2], leading to elevated N and P inputs in soils. Increased inputs of exogenous N and P enhance the soil N and P availability, thereby modifying the primary productivity of terrestrial ecosystems, the composition and activity of soil microbial communities, and the decomposition and sequestration of organic matter. This exerts a profound impact on soil carbon (C) cycling and sequestration, a key link in ecosystem C cycling [3,4].
Soil organic carbon (SOC) stability is crucial for the cycling of C within soil systems [5]. Understanding the mechanisms behind SOC stabilization, decomposition, and sequestration requires categorizing SOC into distinct fractions with varying stabilities and turnover rates [6,7]. Loginow et al. pioneered the use of potassium permanganate oxidation to mimic enzyme degradation during microbial decomposition, dividing SOC into four fractions with different susceptibilities to oxidation [8]. These fractions include the very labile soil oxidizable C fraction (C1), labile soil oxidizable C fraction (C2), less labile soil oxidizable C fraction (C3), and non-labile soil oxidizable C fraction (C4). The labile C fractions (C1 and C2) are highly prone to oxidation and primarily comprise polysaccharides, recently decomposed organic matter, fungal hyphae, and other microbial by products [9]. Oxidizing SOC releases mineral nutrients and facilitates the movement of carbon dioxide (CO2) from the soil to the atmosphere, directly impacting the soil quality and C storage [10,11,12]. Conversely, the stable C fractions (C3 and C4) possess durable molecular structures that persist in the soil for extended periods through mineral association, significantly contributing to SOC sequestration [13,14].
Different fractions of SOC exhibit varied responses to nutrient inputs [15]. Long-term additions of N and P can lead to a decrease in the labile C fractions of SOC in the surface layers of soil, while simultaneously increasing the accumulation of the stable C fractions [16]. However, conflicting research findings exist, with some studies indicating that the addition of NP does not significantly impact the content of oxidizable SOC fractions, but enhances SOC stability [10]. Additionally, there are studies suggesting that fertilization increases both SOC and labile C fractions but reduces SOC stability [17]. Alterations in SOC content, composition, and stability resulting from the enrichment of N and P may influence the C balance in global ecosystems [18]. Although there has been extensive study conducted on the impact of N and P availability on different oxidizable fractions and SOC stability, findings have been inconsistent, and the underlying mechanisms are still not fully understood [19,20,21,22,23].
The Qinghai–Tibet Plateau is the largest geomorphic unit in Eurasia and one of the main permafrost regions at a low latitude [24]. Alpine grassland ecosystems are the most important ecosystem type, pivotal in their role of preserving soil function as a C pool and maintaining regional and even global ecological security [25,26]. Nevertheless, alpine meadows exhibit a heightened sensitivity to global climate change [27]. In recent years, human activities, such as atmospheric deposition and nutrient supplementation, have changed the N and P availability in alpine grasslands [28], profoundly affecting the structure and function of these ecosystems and influencing the soil, thereby affecting nutrient cycling. Therefore, studying the ecological characteristics of alpine grasslands in the Qinghai–Tibet Plateau in the context of climate change is imperative. This study conducted a nine-year fertilization experiment with N and P to assess their impacts on soil enzyme activities, the stability of SOC at three different soil depths, and associated driving factors. The aim was to elucidate the driving mechanisms of N and P additions on SOC stability and its transformation processes, thereby providing new insights into the stability mechanism of C pools in alpine grassland ecosystems in cold regions and ecosystem C pool restoration.

2. Materials and Methods

2.1. Site Description

The study was carried out in 2009 at the Haibei Alpine Grassland Ecosystem Research Station (37°36′ N; 101°19′ E; elevation 3215 m) in Menyuan Hui Autonomous County, Qinghai Province, within the northeastern segment of the Qinghai–Tibet Plateau. The climate in this region is characterized by a plateau monsoon climate, influenced by its geographical location and topography. Summers are warm and brief, while winters are cold and long, with significant temperature variations. The average annual temperature is around 2 degrees Celsius. The coldest month is typically January, with average temperatures ranging from −15 to −5 degrees Celsius, while the hottest month is July, with average temperatures ranging from 8 to 15 degrees Celsius. The annual precipitation is about 500 mm, with 80% of the rainfall occurring between May and September, indicating a higher precipitation during the summer months. The soil type in the study is Gelic Cambisol. The vegetation characteristics under various treatments are summarized in Table 1.

2.2. Nutrient Addition Treatments

In May 2009, we selected a typical grazing area belonging to local pastoralists’ summer pastures and implemented a nutritional supplementation trial using a randomized block design. The experimental area comprised six blocks, each measuring 6 m × 6 m. These blocks were separated by 1 m wide buffers to minimize interference from adjacent plots. Each block included the following nutrient addition treatments: CK (no addition), N addition (10 g urea per square meter each year), P addition (5 g heavy superphosphate per square meter each year), and NP addition (10 g urea and 5 g heavy superphosphate per square meter each year). Since 2009, fertilizers have been evenly spread over the plots on 20 June of each year.

2.3. Soil Sampling

Following the removal of surface litter, soil samples were collected randomly at depths of 0–5 cm, 5–10 cm, and 10–20 cm in August 2018. Samples from 3 soil cores at the same depth were collected from each plot, combined, and placed into numbered zip lock bags. The samples were kept in a refrigerated environment and transferred to the lab for analysis. Upon arrival, the soil samples underwent the process of sieving using a 2 mm screen, resulting in their division into two separate sections. One part was dried naturally, filtered through sieves with apertures of 1 mm and 0.25 mm, and analyzed for the soil chemical properties, including SOC, total nitrogen (TN), total phosphorus (TP), dissolved organic carbon and nitrogen (DOC and DON), soil oxidizable C fractions, and pH. The additional sample was kept at a temperature of 4 °C in order to analyze the soil enzyme activities, including β-1,4-glucosidase (BG), cellulase (CBH), N-acetyl-β-D-glucosaminidase (NAG), leucine aminopeptidase (LAP), and phosphatase (AP).

2.4. Chemical Analyses

SOC was determined via dichromate oxidation. Soil organic matter underwent oxidation with excess potassium dichromate sulfuric acid (K2Cr2O7-H2SO4) solution at 180 °C for 5 min, then titrated with ferrous sulfate (FeSO4) standard solution after cooling, and the SOC content was determined based on the consumption [29]. TN was quantified using the Kjeldahl method, which involved digesting the soil sample with a catalyst and H2SO4. The resulting solution was then analyzed using an automatic nitrogen analyzer for distillation and titration [30]. TP was measured using molybdenum antimony blue colorimetry. The air-dried soil sample was digested with H2SO4 and perchloric acid, and after standing overnight, the upper natant of the digestion solution was determined at a wavelength of 700 nm to obtain the total phosphorus content [31]. The soil pH was determined using an electronic pH meter (1:2.5 soil:water) (Metrohm 702, Herisau, Switzerland). DOC and DON were shaken with fresh soil mixed with distilled water (1:3 soil:water) for 30 min, centrifuged and filtered, and the filtrate was determined using a Liquid TOC II Analyzer (Elementar Analysensysteme GmbH, Hanau, Germany). Microbial biomass C, N, and P (MBC, MBN and MBP) concentrations were measured using the chloroform fumigation extraction method. Two soil samples, one of which was extracted with K2SO4 solution while the other was fumigated with chloroform to kill microorganisms in the soil and then soaked with K2SO4 solution, and the contents of C and N were determined by a Liquid TOC II Analyzer. The fumigation process for MBP was the same as that for MBC and MBN, but it was extracted with NaHCO3 and determined by colorimetry. The difference between the treated and untreated soil samples was utilized for computing the MBC, MBN, and MBP concentrations [32,33]. The soil enzyme activities were assessed via a revised version of conventional fluorometric methods [34,35]. Concisely, 3 g of soil was put into 125 mL of Tris buffer and shaken for 1 h. The samples and buffer were then added to the microplate, shaken well, and incubated at 25 °C. Among these enzymes, alanine, leucine, and phosphatase were incubated for 2 h, and the remaining enzymes were incubated for 4 h. Fluorescence was measured using a fluorescence plate reader (Spectra Max M2, Molecular Devices, San Jose, CA, USA).
The determination of soil oxidizable C fractions was conducted employing the KMnO4 oxidation method [8]. Simply, soil samples containing 15 mg of SOC were placed into three centrifuge tubes. Then, 10 mL of KMnO4 solution was added to each tube with concentrations of 33 mmol/L, 167 mmol/L, and 333 mmol/L, respectively. After shaking for 1 h, the samples were centrifuged at 4000 r/min for 5 min, after which, they were filtered. The filtrate was diluted 250-fold with distilled water, and its absorbance was measured at 565 nm employing a spectrophotometer. The measured soil oxidizable C fractions obtained from the KMnO4 solutions with concentrations of 33 mmol/L, 167 mmol/L, and 333 mmol/L were denoted as Cfrac1, Cfrac2, and Cfrac3 respectively. Cfrac1 was represented by C1, C2 = Cfrac2 − Cfrac1, C3 = Cfrac3 − Cfrac2, and C4 = SOC − Cfrac3.

2.5. Calculation of Relationships among Parameters

The SOC stability index (SI) was calculated as follows [8]:
SI = (C3 + C4)/(C1 + C2)
The other related indices were obtained using the following equations [36]:
C pool index (CPI) = SOCtreatment/SOCck
C pool lability (L) = Cfrac3/ SOC − Cfrac3
Lability index (LI) = Ltreatment/Lck
C pool management index (CPMI) = CPI × LI × 100

2.6. Data Analysis and Statistical Methods

The experimental data underwent processing using Microsoft Excel. Two-way analysis of variance (ANOVA) was employed to compare the effects of soil depth, fertilization treatment, and their interaction on the soil enzyme activities, soil oxidizable C fractions, SOC, L, SI, and CPMI (Duncan, p < 0.05). Pearson’s correlation analysis was used to investigate the relationships between the soil enzyme activities, soil oxidizable C fractions, DOC, and MBC. Statistical analyses were conducted using SPSS Statistics 22.0. Figures were generated using Origin 2018 software. Redundancy analysis (RDA) was employed to ascertain the connections between environmental factors (soil chemical properties index and vegetation properties) and species factors (soil oxidizable C fractions, SI, and soil enzyme activities) across different soil layers and under various fertilization treatments. The red line denotes environmental factors, whereas the blue line reflects species factors. Different angles between the arrows serve as indicators of the relationships between variables: acute angles signify positive correlation, obtuse angles denote negative correlation, while right angles suggest uncorrelated values. RDA was conducted using CANOCO 5.0.

3. Results

3.1. Response of Soil Oxidizable C Fractions to Long-Term Fertilization in Different Soil Layers

The soil oxidizable C fractions were ranked in the following order: C3 < C1 and C2 < C4. C1 in CK, N addition, and P addition revealed significant differences among distinct layers (p < 0.05) and gradually decreased with an increasing soil layer. Soil depth and fertilization significantly influenced C1 and C2 (p < 0.05). C1 increased significantly in response to the NP treatment, but not in response to N or P addition at 10–20 cm. N addition and P addition significantly decreased C2 at 0–5 cm, while NP addition decreased C2 at 5–10 cm (p < 0.05). C2 was the highest in CK at 0–20 cm, although the difference was not significant. Furthermore, fertilization, soil depth, and their interaction significantly influenced C3 and C4 (p < 0.05). N and P addition increased C3 at 5–10 cm when compared to that of CK. C4 in the NP addition demonstrated a significant increase compared to CK at 0–10 cm, while N addition and P addition increased C4 at 5–10 cm (Figure 1).

3.2. Response of SOC and C Pool Lability and SI and CPMI after Long-Term Fertilization in Different Soil Layers

Fertilization, soil depth, and their interaction significantly influenced SOC, C pool lability, and CPMI (p < 0.05). Furthermore, fertilization and the interaction between fertilization and soil depth significantly influenced SI (p < 0.05). After 9 years, NP addition significantly increased SOC, but decreased L at 0–10 cm. (Figure 2a,b). NP addition significantly raised the SI compared to CK at 0–10 cm (Figure 2c). N addition resulted in a significantly lower CPMI than the CK at 0–5 cm. Nevertheless, N addition, P addition, and NP fertilization resulted in a significant decline in the CPMI in comparison to CK at 5–10 cm. No significant variations were observed in the CPMI at 10–20 cm between CK and the fertilizer treatment groups (Figure 2d).

3.3. Response of Soil Extracellular Enzyme Activities to Long-Term Fertilization in Different Soil Layers

Fertilization, soil depth, and their interaction had significant effects on the activities of BG, CBH, and NAG. Additionally, fertilization and the interaction between fertilization and depth significantly influenced the activities of LAP and AP. Neither P nor N fertilization significantly affected BG enzyme activity. However, NP addition significantly increased the BG enzyme activity compared to that in CK at 0–10 cm (p < 0.05). NP addition significantly increased CBH and NAG compared to CK at 0–5 cm. NP addition increased the LAP activity significantly, relative to that of the CK at 5–10 cm (p < 0.05). Compared to CK, NP addition significantly increased the AP at 0–10 cm, whereas P addition decreased the AP at 0–20 cm (Figure 3).

3.4. Factors Influencing Soil Enzyme Activities and Soil Oxidizable C Fractions

RDA was conducted to explore the connections between environmental factors and soil oxidizable C fractions, as well as soil enzyme activities. The vegetation and soil chemical properties accounted for 63.8% of the variation in soil oxidizable C fractions (Figure 4a). The first two axes show 39.5% and 16% of the variation, respectively. Specifically, SOC showed 37.6% of the variation, while TN, MBC, TP, pH, MBP, aboveground biomass (AGB), NAG, BG, CBH, and MBN showed 18.8%, 18.5%, 13.8%, 12.8%, 7.6%, 6.8%, 6.7%, 6.5%, 5.9%, and 5.8% of the variation, respectively. AGB, SOC, TN, MBC, MBN, MBP, TP, belowground biomass (BGB), BG, CBH, NAG, and AP exhibited positive correlations with C1, C3, C4, and SI, while LAP and pH exhibited negative correlations with these components. Specifically, SOC, TN, MBC, MBN, MBP, belowground biomass (BGB), BG, CBH, NAG, and AP were positively correlated with C2, while AGB, TP, LAP, and pH were negatively correlated with C2.
The vegetation and soil chemical properties accounted for 20.1% of the variation in enzyme activities (Figure 4b). The first two axes show 15.4% and 3.5% of the variation, respectively. SOC alone contributed 6.9% of the variance, while pH (6.5%), TN (5%), and AGB (5%) also showed notable contributions. SOC, TN, TP, DON, BGB, and AGB exhibited positive correlations with BG, CBH, and NAG, while DOC and pH exhibited negative correlations with BG and NAG. Specifically, DOC, pH, AGB, BGB, DON, and SOC were positively correlated with LAP and AP, while TN and TP were negatively correlated with LAP and AP.

4. Discussion

4.1. Effects of Long-Term Fertilization on Soil Oxidizable C Fractions and SOC Stability

In this study, compared to CK, NP increased SOC, C4, and SI, but decreased C2, CPMI, and L at a 0–10 cm depth. Fertilization positively influenced aboveground biomass, whose residues contribute to organic matter sources, often resulting in an increased SOC when returned to the soil [37]. Despite NP application increasing the C input to plants, it accelerated the decomposition of labile SOC fractions due to enhanced soil microbial activity and microbial biomass, as evidenced by higher enzyme activities at a 0–10 cm depth [38,39]. Moreover, CPMI at a depth of 0–10 cm decreased with NP addition, indicating a decline in the soil quality due to long-term NP fertilization. These findings suggest that, while NP addition enhanced the SOC stability in the surface layer, it likely depleted the labile C fractions, thereby reducing the soil quality, consistent with the findings of Bhattacharyya et al. [40]. At the depth of 10–20 cm, compared to CK, the CPMI of the N, P, and NP addition groups was greater than 1. Additionally, the C1 of the NP addition group was greater compared to the P addition and CK groups, indicating that NP addition promoted very labile soil oxidizable C fraction accumulation and improved the soil quality. Similar observations were made by Rudrappa et al., who found significantly higher soil oxidizable C fractions after NP addition compared to CK [15]. Furthermore, NP addition stimulated the growth of aboveground biomass compared to other treatments, thereby increasing the turnover rate of SOC and activating the C pool [16,41]. RDA indicated that the soil nutrient levels, aboveground biomass, and C and N source enzyme activities were the main drivers influencing variations in oxidizable C fractions and SOC stability, suggesting that a higher C input increases the soil oxidizable C fractions and SOC stability.
C1 and SOC in the 0–10 cm soil layer were higher than those in 10–20 cm layer. This discrepancy may have been due to the upper soil layer receiving more residue input and undergoing faster decomposition compared to the deeper layers, thereby providing additional sources of labile C fractions and SOC [42,43]. Although it was anticipated that NP addition would increase both the labile and stable C fractions, reducing the SOC stability after long-term fertilization, the labile C fractions at depths of 0–10 cm and 10–20 cm resulted in the opposite outcome instead, possibly because the labile C fractions were more unstable in the topsoil and prone to oxidation loss [44,45].

4.2. Effects of Long-Term Fertilization on Enzyme Activities

Soil enzymes, primarily produced by microorganisms, play crucial roles in SOC decomposition, formation, and nutrient cycling [46]. Enzymes such as BG and CBH are pivotal in C cycling, with CBH hydrolyzing cellulose to release cellobiose, and BG completing the last phase of cellulose breakdown by converting cellobiose into glucose [47,48]. NP fertilization notably increased the BG and AP activities at 0–10 cm and CBH and NAG at 0–5 cm, which aligns with previous findings [49,50,51]. Soil enzymes originate mostly from root exudation, humus, and soil microorganisms [52,53], with fertilization enhancing both aboveground and belowground biomass, thus influencing enzyme synthesis through root exudate release into the rhizosphere [54,55]. Niemi et al. demonstrated a positive relationship between plant root biomass and enzymatic activity [56].
Soil enzyme activities typically correlate with SOC content [57], as high SOC levels enhance the soil properties and stimulate microbial activity, thereby promoting enzyme synthesis [58]. Moreover, SOC contributes to creating favorable conditions for soil enzymes by forming stable complexes with free enzymes [59]. This study confirmed a positive link between the enzyme and SOC content. The enzyme activities were also influenced by the soil pH, with significant negative correlations observed due to decreased microbial activity [60], emphasizing the role of the pH in altering the soil microbial activity and organic matter decomposition from various plant residue inputs.
Substrate availability strongly influences the soil enzyme activities [52,61], with energy and substrates for enzyme synthesis and degradation often originating from plant roots and litter [62]. The RDA results indicated that aboveground biomass and soil nutrient content were the principal factors affecting the BG, CBH, NAG, LAP, and AP activities following long-term fertilization. NP fertilization notably increased the aboveground and belowground biomasses, with plant litter accumulation providing abundant substrates for microorganisms.
The significant correlations among the BG, CBH, NAG, LAP, and AP activities suggest similar origins and C sources in the soil [63]. BG exhibited a higher enzymatic activity among the two C-cycling enzymes, showing a stronger correlation with SOC and its oxidizable fractions than CBH, thus highlighting BG’s greater role in soil C transformation, especially in cellulose decomposition. P addition negatively impacted AP but did not affect BG at a 0–10 cm depth [64,65,66], possibly due to inorganic P inputs that reduced the microbial reliance on organic P mineralization via AP [67]. NP addition significantly increased AP compared to P addition alone, suggesting that N addition enhanced P addition’s stimulating effect on AP. Overall, the soil nutrients and aboveground biomass were principal factors influencing the soil enzyme activities following long-term fertilization.

4.3. Relationship of Soil Oxidizable C Fractions with Soil C-Cycle Enzyme Activities

Soil enzyme activities serve as indicators of microbial growth and activity, as they directly influence C and N cycling in the soil [68,69]. Each enzyme has its specific substrate and catalytic capability for particular biochemical reactions [70]. In this study, the activities of enzymes related to C and N cycling showed positive associations with SOC. Since the substrates for enzymatic reactions in the soil mainly comprise organic matter, an increase in the SOC content can enhance BG and CBH activity, thus promoting the soil C cycle [71]. An elevated organic matter content typically leads to increased enzyme activities [72]. Generally, CBH degrades cellulose to generate labile organic C [73], which is essential for the formation of labile fractions in SOC. The positive correlations between BG, CBH, and NAG with C1 and C2 indicate their involvement in promoting labile organic C formation and increasing SOC mineralization [74]. These results corroborate previous research conducted by Zhang et al., who reported a positive association between BG and permanganate-oxidizable C [75]. Similarly, Li et al. observed significant associations between CBH, BG, and soil oxidizable C fractions in the soil [70], likely because C1 and C2 can serve as C sources for soil microorganisms, thereby boosting enzyme activities. Additionally, soil enzymes can form humus–protein complexes with SOC and its oxidizable fractions, protecting them from decomposition.
MBC and DOC are principal indicators of labile organic C [76,77]. Nevertheless, no significant correlation was observed between DOC and either BG or CBH activity (p > 0.05, Table 2), suggesting that DOC likely served as both a substrate and product in enzymatic reactions [78]. BG catalyzes glucose production from oligosaccharides, the product which is swiftly consumed via microbial assimilation and metabolism, resulting in a decrease a in DOC [79]. MBC exhibited a significant positive correlation with BG and NAG, indicating that microbes are the primary sources of these enzymes. Fluctuations in the soil chemical properties and nutrient levels may also influence the microbial community structure and activity, thereby modulating microbial biomass and the release of extracellular enzymes into the soil by microbial biomass [80]. Several studies have observed a robust positive association between enzymatic activities and microbial biomass [81,82,83]. Thus, our findings suggest that higher SOC and soil oxidizable C fraction values may facilitate soil microorganism growth, thereby increasing enzyme synthesis and release.

5. Conclusions

Mixed N and P addition significantly enhances SOC stability by increasing the content of soil C4. However, individual N addition decreases the soil quality by affecting the contents of C2 and C3. The vegetation aboveground biomass and soil C enzyme activities are critical factors influencing the soil C stability, as they can impact plant litter and soil microbial activities. Our findings provide valuable insights into the dynamics of soil carbon stability in grassland ecosystems.

Author Contributions

Conceptualization, formal analysis, data curation, writing—original draft, visualization, J.Z.; methodology, resources, H.Z.; formal analysis, investigation, Y.W.; validation, supervision, funding acquisition, S.X. and G.W.; project administration, S.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China Joint Fund Project (U21A20186), the Second Qinghai Tibet Plateau Comprehensive Scientific Research Project (2019QZKK0302-02), and the Shaanxi Creative Talents Promotion Plan-Technological Innovation Team (2023-CX-TD-37).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Acknowledgments

The authors also thank Wenjing Chen, Yuanze Li, Leilei Qiao, and Ziwen Zhao for their assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 14 01376 g001
Figure 1. Illustrates the variations in (a) C1, (b) C2, (c) C3, and (d) C4 across different soil layers among fertilization treatments and the control (CK). The treatments include CK, N, P, and NP. Different letters denote significant differences at p < 0.05. Data are presented as mean ± standard error.
Figure 1. Illustrates the variations in (a) C1, (b) C2, (c) C3, and (d) C4 across different soil layers among fertilization treatments and the control (CK). The treatments include CK, N, P, and NP. Different letters denote significant differences at p < 0.05. Data are presented as mean ± standard error.
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Figure 2. Comparison of (a) soil organic carbon (SOC), (b) C pool lability (L), (c) SOC stability index (SI), and (d) C pool management index (CPMI). Different letters denote significant differences at p < 0.05.
Figure 2. Comparison of (a) soil organic carbon (SOC), (b) C pool lability (L), (c) SOC stability index (SI), and (d) C pool management index (CPMI). Different letters denote significant differences at p < 0.05.
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Figure 3. Comparison of (a) activity of β−1,4−glucosidase (BG), (b) cellulase (CBH), (c) leucine aminopeptidase (LAP), (d) Nacetyl−β−D-glucosaminidase (NAG), and (e) phosphatase (AP). Different letters denote significant differences at p < 0.05.
Figure 3. Comparison of (a) activity of β−1,4−glucosidase (BG), (b) cellulase (CBH), (c) leucine aminopeptidase (LAP), (d) Nacetyl−β−D-glucosaminidase (NAG), and (e) phosphatase (AP). Different letters denote significant differences at p < 0.05.
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Figure 4. Redundancy analysis (RDA) was conducted to elucidate the relationships between environmental factors and (a) soil oxidizable C fractions, as well as (b) soil enzyme activities. Variables considered in the analysis include SOC, total nitrogen (TN), total phosphorus (TP), microbial biomass carbon, nitrogen, and phosphorus (MBC, MBN, and MBP), aboveground biomass (AGB), belowground biomass (BGB), BG, CBH, NAG, LAP, AP, C1, C2, C3, C4, and SI.
Figure 4. Redundancy analysis (RDA) was conducted to elucidate the relationships between environmental factors and (a) soil oxidizable C fractions, as well as (b) soil enzyme activities. Variables considered in the analysis include SOC, total nitrogen (TN), total phosphorus (TP), microbial biomass carbon, nitrogen, and phosphorus (MBC, MBN, and MBP), aboveground biomass (AGB), belowground biomass (BGB), BG, CBH, NAG, LAP, AP, C1, C2, C3, C4, and SI.
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Table 1. Vegetation parameters under different treatments.
Table 1. Vegetation parameters under different treatments.
TreatmentsDiversity IndexAbundanceAboveground BiomassBelowground Biomass
CK2.5350 a17.3333 a555.9183 b1.6034 bc
N1.9050 b11.1667 b577.6633 b2.5324 a
P1.9500 b10.0000 b561.4983 b1.4245 c
NP1.1233 c6.0000 c969.8640 a2.3114 ab
Note: CK = no addition; N = nitrogen addition; P = phosphorus addition; NP = combined nitrogen and phosphorus addition; lowercase letters indicate differences among four treatments (p < 0.05).
Table 2. Correlation of enzyme activities and soil oxidizable C fractions.
Table 2. Correlation of enzyme activities and soil oxidizable C fractions.
C1C2C3C4SOCDOCMBCBGCBHNAGLAPAP
C11
C20.664 **1
C30.409 **0.241*1
C40.685 **0.395 **0.367 **1
SOC0.769 **0.513 **0.470 **0.985 **1
DOC−0.365 **−0.347 **−0.376 **−0.177−0.251 *1
MBC0.758 **0.465 **0.351 **0.645 **0.691 **−0.1191
BG0.403 **0.241 *0.1820.524 **0.526 **−0.1700.346 **1
CBH0.257 *0.1670.1500.458 **0.446 **−0.0180.2310.786 **1
NAG0.462 **0.342 **0.1690.434 **0.458 **−0.1210.391 **0.593 **0.531 **1
LAP−0.085−0.046−0.189−0.034−0.057−0.022−0.1600.591 **0.546 **0.375 **1
AP0.0830.032−0.0990.255 *0.221−0.021−0.0250.608 **0.555 **0.427 **0.602 **1
* Indicates a significant difference at p < 0.05; ** Indicates a significant difference at p < 0.01. SOC: soil organic carbon; DOC: dissolved organic carbon; MBC: microbial biomass carbon; BG: β-1,4-glucosidase; CBH: cellulase; NAG: N-acetyl-β-D-glucosaminidas; LAP: leucine aminopeptidase; and AP: phosphatase.
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Zhai, J.; Zhou, H.; Wu, Y.; Wang, G.; Xue, S. Effect of Nitrogen and Phosphorus on Soil Enzyme Activities and Organic Carbon Stability in Qinghai–Tibet Plateau. Agronomy 2024, 14, 1376. https://doi.org/10.3390/agronomy14071376

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Zhai J, Zhou H, Wu Y, Wang G, Xue S. Effect of Nitrogen and Phosphorus on Soil Enzyme Activities and Organic Carbon Stability in Qinghai–Tibet Plateau. Agronomy. 2024; 14(7):1376. https://doi.org/10.3390/agronomy14071376

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Zhai, Jiaying, Huakun Zhou, Yang Wu, Guoliang Wang, and Sha Xue. 2024. "Effect of Nitrogen and Phosphorus on Soil Enzyme Activities and Organic Carbon Stability in Qinghai–Tibet Plateau" Agronomy 14, no. 7: 1376. https://doi.org/10.3390/agronomy14071376

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