Exogenous Carbon Type Determines the Structure and Stability of Soil Organic Carbon in Dryland Farmlands Under a Continental Semi-Arid Climate

Abstract

The effects of different exogenous carbon types on the chemical structural characteristics and stability of soil organic carbon in dryland farmland remain unclear. Based on a four-year fixed-site experiment in a typical dryland farmland on China’s Loess Plateau, this study systematically analyzed the impacts of different carbon sources on soil enzyme activities, organic carbon content, chemical structural characteristics, and their interrelationships under five treatments: (i) no fertilization (T₀); (ii) 100% chemical nitrogen, phosphorus, and potassium fertilizers (CK); (iii) 50% CK + fermented cattle manure (T₁); (iv) 50% CK + corn straw (T₂); (v) 50% CK + mixed fermented cattle manure/corn straw (T₃). The results showed that the activities of β-glucosidase and N-acetylglucosidase ranked in the order T₁ > T₂ > T₃ and T₃ > T₂ > T₁, respectively. Specifically, β-glucosidase activity under T₁ increased by 35.26% compared to CK, while N-acetylglucosidase activity under T₃ increased by 30.78% relative to CK. Compared to CK, the T₁, T₂, and T₃ treatments increased soil organic carbon by 26.84%, 11.27%, and 18.63%, and alkyl carbon content by 7.67%, 2.91%, and 5.57%, respectively. Additionally, T₁ and T₃ treatments elevated aromatic carbon content by 20.59% and 176.47% relative to CK. The organic carbon activity index under T₁ was the lowest, decreasing by 10.04% compared to CK. Structural equation modeling (SEM) path analysis revealed that the addition of different exogenous carbon sources in dryland farming primarily influenced the structure and stability of soil organic carbon by directly or indirectly enhancing the activities of glucosidase, β-acetylglucosidase, and alkaline phosphatase, with T₁ demonstrating the most significant improvement.

1. Introduction

The Loess Plateau is one of China’s typical rainfed agricultural regions. This area faces a series of challenges, including severe soil and water erosion, poor soil aggregate structure, and low precipitation utilization efficiency [1,2,3]. These issues lead to low soil organic carbon (SOC) content, weak water retention and nutrient retention capacity, and low land productivity in dryland farmlands, severely constraining the sustainable development of rainfed agriculture in this region [4,5]. The application of exogenous carbon inputs, such as agricultural by-products (e.g., crop straw) and livestock waste (e.g., animal manure), into farmland is a critical ecological measure to increase SOC content, enhance soil carbon sequestration, and synergistically achieve agricultural waste recycling and soil carbon enrichment [5,6,7]. Extensive research demonstrates that inputs of crop straw, animal manure, biochar, and other exogenous carbon sources not only boost SOC content and improve soil fertility but also enhance soil carbon sequestration capacity and reduce CO₂ emissions [8,9,10,11]. Therefore, investigating the effects of different types of exogenous carbon inputs on the chemical structure and stability of SOC in dryland farmlands is of great significance for promoting soil carbon sequestration, enhancing soil fertility, and improving soil ecosystem health in the Loess Plateau’s rainfed agricultural systems.

Soil organic carbon (SOC) is a critical factor influencing soil health, including soil fertility enhancement, soil aggregate formation, water retention, and greenhouse gas emission regulation [12,13,14]. The chemical structure and stability of SOC are key to understanding soil fertility and carbon cycling [15]. Studies have shown that the chemical structure and stability of SOC are affected by multiple factors such as crop type, fertilization practices, and soil type, with exogenous carbon inputs being one of the most significant factors influencing SOC content and structural composition [16,17]. Currently, the addition of exogenous carbon is recognized as an important strategy for soil carbon enrichment and fertility improvement. Ding et al. [18] demonstrated that biochar application significantly alters the chemical structure of SOC, primarily by increasing the proportion of aromatic carbon, thereby enhancing SOC stability. Conversely, Wang Xuexia et al. [19] found that straw incorporation increases the fractions of easily decomposable alkoxy carbon and carbonyl carbon while reducing the proportions of recalcitrant alkyl carbon and aromatic carbon, leading to decreased SOC stability.

Soil enzymes play a vital role in carbon and nitrogen cycling and material transformation processes, significantly influencing soil functionality and ecosystem balance in rainfed farmlands [20]. Soil enzyme activity is critical for the decomposition of soil organic carbon (SOC) and nutrient transformation [21], serving as a key indicator for assessing soil quality and ecosystem health [22,23]. Moreover, soil enzyme activity profoundly affects changes in SOC content, structure, and stability [24,25], while the addition of exogenous carbon inputs can significantly enhance enzyme activity [26]. Jiang et al. [27] demonstrated that exogenous carbon inputs markedly increase the activities of glucosidase, cellulase, and β-N-acetylglucosaminidase (NAG), thereby altering the composition of SOC. Studies also indicate that under maize straw incorporation, cellulase activity is positively correlated with alkyl carbon and aromatic carbon fractions [28].

The combined application of exogenous carbon and nitrogen can significantly enhance soil enzyme activities [29,30], promoting nutrient release from maize straw and thereby improving soil fertility. Yu et al. [31] found that long-term manure incorporation significantly increases labile soil organic carbon (SOC) content, which subsequently boosts rice yield. The incorporation of organic fertilizers such as cattle manure and swine manure not only markedly elevates soil microbial activity but also enhances associated soil enzyme activities [32], laying the foundation for improved soil quality. However, in the practical context of rainfed farmland management, diverse exogenous carbon inputs—including agricultural by-products and livestock waste from various sources and types—are typically applied to soils as mixed formulations. The impacts of such mixed exogenous carbon inputs on the chemical structure, stability of soil organic carbon (SOC), and related influencing factors remain insufficiently explored. To address this knowledge gap, a four-year field experiment was conducted in Xiji County, Ningxia Province—a representative rainfed agricultural area on China’s Loess Plateau—to investigate the effects of fermented cattle manure and maize straw incorporation. The objectives of this study were as follows: (1) to analyze the impacts of different exogenous carbon types on soil nutrients, organic carbon content, and enzyme activities; (2) to investigate the functional group structure and stability of soil organic carbon; (3) to elucidate the interrelationships among soil organic carbon stability, enzyme activities, and soil nutrients, identifying key determinants of soil organic carbon stability. This research seeks to provide theoretical foundations and technical strategies for enhancing soil fertility and optimizing agricultural waste utilization in dryland farmlands of southern Ningxia’s mountainous regions.

2. Materials and Methods

2.1. Experimental Site and Design

The trial was started in 2021 in Xiji County, Ningxia Hui Autonomous Region, China. Xiji County is located at the northwest end of the Loess Plateau and is a typical dryland farming area in China. The research farm was located at latitude 35°56′39″ N, longitude 105°30′11″ E (Figure 1), and has an altitude of 1954 m. The research utilized the four-year positioning experimental data from 2021 to 2024. The farm was located within the temperate continental semi-arid climate with a mean annual temperature of 6 °C, mean annual rainfall of 400 mm, mean annual evaporation of 2000 mm, and a frost-free period of about 140 days per annum. It had a large temperature difference between day and night and abundant light. The experimental field follows a “one crop a year” system, and the soil type is loessial soil. Before the experiment started (in April 2021), the soil of the plow layer (0–20 cm) was collected, and the initial physical and chemical properties of the soil were determined. The specific data are shown in

Table 1. Basic soil properties of the experimental field.

Soil Depth (cm)	TN (g kg−1)	AP (mg kg−1)	AK (mg kg−1)	SOM (g kg−1)
0–20	0.9 ± 0.1	30.1 ± 1.5	81.1 ± 3.4	14.1 ± 0.8

TN: total nitrogen; AP: available phosphorus; AK: available potassium; SOM: soil organic matter.

. Meanwhile, a small weather station was installed in the experimental field to monitor the daily rainfall and temperature in the experimental field. The temporal variations in daily rainfall and air temperature throughout the corn growing season (April to October 2024) are illustrated in Figure 2.

2.2. Experimental Design

The experiment takes the local main crop maize (Ningdan 19) as the research object and adopts a completely randomized block design. A total of five treatments are set: no fertilization, conventional chemical fertilizer, 50% conventional chemical fertilizer + 12,000 kg ha⁻¹ fermented cow dung, 50% conventional chemical fertilizer + 9686 kg ha⁻¹ maize straw (with the same carbon as fermented cow dung), and 50% conventional chemical fertilizer + 6000 kg ha⁻¹ fermented cow dung + 4843 kg ha⁻¹ maize straw (with the same carbon as fermented cow dung). Each treatment was replicated three times, totaling 15 experimental plots, with each plot covering an area of 36 m² (6 m × 6 m). The plots were separated from one another using 1.0 m-deep and 0.6 m-wide cemented hedge barriers. The planting pattern adopted ridge-furrow plastic mulching (Figure 3), with a ridge width of 0.8 m and a planting density of 52,500 plants per hectare (ha⁻¹). Detailed fertilizer application rates for each treatment are shown in

Table 2. Fertilizer application amounts under different treatments.

Treatment	Chemical Fertilizer (kg ha−1)			Fermented Cattle Manure kg ha−1	Corn Straw kg ha−1
	N	P2O5	K2O
N	0	0	0	0	0
CK	240	150	120	0	0
C	120	75	60	0	9686
M	120	75	60	12,000	0
C/M	120	75	60	6000	4843

. Nitrogen fertilizer was applied at a basal-to-topdressing ratio of 7:3, with topdressing applied during the maize jointing stage. Phosphorus fertilizer, potassium fertilizer, fermented cattle manure, and corn stover were all applied as a one-time basal application. The chemical fertilizers applied to all treatment groups maintain the same nutrient content levels (

Table 3. Nutrient content of different fertilizers.

Fertilizer Name	Nutrient Content (%)
	N	P2O5	K2O	C
Urea	46	/	/	/
Ammonium phosphate dibasic	15	48	/	/
Potassium sulfate	/	/	52	/
Fermented cattle manure	0.78	0.97	1.01	36.86
Corn straw	0.51	0.34	1.20	45.67

). The fermented cattle manure was obtained through composting raw manure collected from local livestock farmers, while the corn stalks were also sourced from crops grown by local farmers. The specific nutrient contents of the organic materials are detailed in Table 3. Other field management practices, such as weeding and pesticide application, were consistent with those employed by local farmers.

2.3. Measurement and Calculation of Indicators

After harvest (8 October 2024), collect soil samples from the 0–20 cm soil layer using a soil auger following the five-point sampling method. Randomly take 5 auger cores from each plot and thoroughly mix them. Bring the soil samples back to the laboratory and sieve them through a 2 mm mesh to remove plant roots, stones, and other impurities. Divide the soil samples into two parts: ① Place approximately 100 g of soil into a self-sealing bag and store it in 0–20 °C freezer for measuring soil β-glucosidase (BG), N-acetyl-β-D-glucosaminidase (NAG), alkaline phosphatase (AKP), and cellobiohydrolase (CBH). ② Air-dry the remaining soil for later use in determining soil organic carbon (SOC), nutrient content, and the chemical structure of organic carbon.

Soil organic carbon (SOC) was determined using the K₂Cr₂O₇ volumetric method–external heating method [33,34]: approximately 0.2 g of air-dried soil (passed through a 100-mesh sieve, weighed to 0.1 mg precision) was placed into a dry flat-bottom digestion tube. Then, 5.00 mL of potassium dichromate solution (39.2245 g of K₂Cr₂O₇ dried at 105 °C dissolved in 1 L of distilled water, concentration C (1/6 K₂Cr₂O₇ = 0.800 mol L⁻¹)) and 5.00 mL of concentrated sulfuric acid (H₂SO₄, ρ = 1.84 g cm⁻³) were accurately added using a pipette gun. The tubes were then placed in an electrolyzer and heated at 190 °C for 16–20 min (heating can be started first, and timing begins once the temperature reaches 190 °C). Note that two blanks (reagents only, no soil sample) were included in each batch of digestion. A bent-stem funnel was placed on each digestion tube to act as a condenser. After digestion, the tubes were placed on a stainless-steel rack and allowed to cool naturally to room temperature. The digested mixture was then completely transferred into a clean wide-mouth Erlenmeyer flask (during this process, a wash bottle filled with distilled water was used to rinse and transfer the contents; tilting the flat-bottom tube and rinsing the bottom directly with water ensured complete transfer). The total volume in the Erlenmeyer flask after transfer should ideally be 50–60 mL. Next, 2–3 drops of phenanthroline indicator (1.485 g of phenanthroline (C₁₂H₈N₂·H₂O) and 0.695 g of ferrous sulfate (FeSO₄·7H₂O) dissolved in 100 mL of water, without drying the reagents, stored in a brown bottle) were added. The remaining K₂Cr₂O₇ from the digestion reaction was titrated using a ferrous sulfate solution (56.0 g of chemically pure FeSO₄·7H₂O and 15 mL of concentrated sulfuric acid dissolved in 1 L of distilled water). Note: This solution is unstable and prone to oxidation, so it must be prepared fresh before titration and standardized immediately before use with 5 mL of a known potassium dichromate mixture (i.e., the two blanks mentioned above). During titration, the solution in the Erlenmeyer flask changes from orange-yellow → green (slowing down as it becomes increasingly light green) → reddish-brown. The initial and final values of the ferrous sulfate solution were recorded, and the difference was calculated. The soil organic carbon content was calculated using the following formula:

$$M_{SOC}={\displaystyle \frac{\frac{C\times V_1}{V_0}(V_0-V)\times {10}^{-3}\times M\times 1.08}{m}}\times 1000$$

(1)

In the formula, M_SOC represents soil organic carbon content (g kg⁻¹); C represents the concentration of the potassium dichromate standard solution (0.800 mol L⁻¹); V₁ represents the volume of potassium dichromate solution added (5.00 mL); V₀ represents the volume of ferrous sulfate solution used for blank titration (mL); V represents the volume of ferrous sulfate solution used for titrating the soil sample (mL); M represents the molar mass of the 1/4 carbon atom (3 g mol⁻¹); 10⁻³ represents the conversion factor from milliliters to liters; 1.08 represents the oxidation correction factor; m represents the mass of air-dried soil (g).

Ammonium nitrogen and nitrate nitrogen were measured using a continuous flow analyzer (SAKLARSAN++/S-011300110537, SKALAR, Amsterdam, The Netherlands). Available phosphorus was extracted using the sodium bicarbonate (NaHCO₃) method and quantified with a spectrophotometer. Available potassium was extracted using ammonium acetate and measured with a flame photometer [35].

In this study, the activities of soil β-glucosidase (BG), N-acetyl-β-D-glucosaminidase (NAG), alkaline phosphatase (AKP), and cellulase (CBH) were determined using a microplate fluorescence assay based on the principle of enzymatic hydrolysis of substrates releasing 4-methylumbelliferone (4-MUB), which is detected fluorometrically [36]. The detailed procedures are as follows:

Suspension preparation: Weigh 2 g of fresh soil into a 50 mL centrifuge tube, add 30 mL of deionized water, and shake on a rotary shaker at 25 °C and 180 rpm for 40 min to fully disperse soil aggregates. Transfer the soil suspension into a 500 mL beaker using 170 mL of deionized water, and homogenize to prepare a uniform soil suspension. Enzyme activity assay: Pipette 200 μL of the soil suspension into a 96-well microplate (8 replicates per sample). Sample wells: Add 50 μL of 200 μmol L⁻¹ substrate. Blank wells: Add 50 μL deionized water + 200 μL soil suspension. Negative control wells: Add 50 μL substrate + 200 μL deionized water. Quenching standard wells: Add 50 μL of 10 μmol L⁻¹ 4-MUB standard + 200 μL soil suspension. Reference standard wells: Add 50 μL of 10 μmol L⁻¹ 4-MUB standard + 200 μL deionized water. Prepare 8 replicates for blanks, negative controls, quenching standards, and reference standards per sample. Incubate the microplate in darkness at 25 °C for 4 h. Terminate the reaction by adding 10 μL of 0.5 mol L⁻¹ NaOH to each well. After 1 min, measure fluorescence intensity using a microplate reader with excitation and emission wavelengths set at 365 nm and 450 nm, respectively. The enzyme activity calculation formula is as follows [37]:

$$A=BV/(e{v}_{1}tm)$$

(2)

$$B=(f-f_0)/Q-f_s$$

(3)

$$E=f_r/(c_0{v}_2)$$

(4)

$$Q=(f_Q-f_0)/f_r$$

(5)

In the formula, A represents soil enzyme activity (nmol·g⁻¹·h⁻¹); B represents the corrected fluorescence value of the sample; V represents the total volume of soil suspension (200 mL); v₁ represents the volume of soil suspension added per microplate well (0.2 mL); t represents dark incubation time (4 h); m represents dry soil mass (converted from 2 g of fresh soil); f represents the fluorescence value of sample wells (measured by microplate reader); f₀ represents the fluorescence value of blank wells; Q represents the quenching coefficient; f_Q represents the fluorescence value of quenching standard wells; f_r represents the fluorescence value of reference standard wells; fs represents the fluorescence value of negative control wells; E represents the fluorescence emission coefficient; c₀ represents the concentration of the reference standard (10 μmol L⁻¹); v₂ represents the volume of reference standard added (0.05 mL).

Soil organic carbon aggregate structure was determined using Fourier-transform infrared spectroscopy (FTIR). Weigh 1 mg of soil sample and mix it with 200 mg of KBr. After grinding the mixture uniformly, press it into a translucent thin film using a tablet press. Scan the film with an IRSpirit-L Fourier-transform infrared spectrometer (Shimadzu Corporation, Kyoto, Japan), with a scanning wavelength range of 400–4000 cm⁻¹, a scanning time of 30 s, and a resolution of 4 cm⁻¹. Each sample was analyzed in three replicates to minimize errors. Subsequently, the characteristic absorption peaks of the Fourier-transform infrared (FTIR) spectra were analyzed and assigned to specific functional groups based on their spectral signatures, as summarized in Table 1 [38,39]. Spectral correction (mineral interference removal) was performed by spectral subtraction: raw soil spectra minus oxidized soil spectra. The subtraction was executed using OMNIC 9.2 software (Thermo Scientific, Waltham, MA, USA) with a subtraction factor of 1 [40]. Baseline correction was conducted as follows: a linear tangential baseline was calculated in OMNIC 9.2 software using local absorbance minima as zero points [41,42]. Six bands representing organic functional groups were selected and measured for absorbance intensity based on prominent absorption peaks and shoulder features in the spectra (see

Table 4. Chemical structure of soil organic carbon (SOC) and characteristic infrared (FTIR) absorption peaks.

Chemical Structure of Soil Organic Carbon	Absorption Peak Positions (cm−1)
O-alkyl-C	1030
Alkyl-C	1430
Aromatic-C	1600~1680
Ketone-C	1700~1800
Carboxyl-C	2500~2710
Phenolic alcohol	3600~3700

O-alkyl-C—alkoxy carbon; Alkyl-C—alkyl carbon; Aromatic-C—aromatic carbon; Ketone-C—ketone carbon; Carboxyl-C—carboxyl carbon.

). To quantify relative changes in absorbance, the intensity of each band was normalized as the percentage of the sum of absorbance intensities of the six bands. Based on the infrared spectroscopy data, the alkyl carbon-to-alkoxy carbon ratio [43] and the organic carbon lability index [44] were calculated.

$$\mathrm{Alkyl}\text{-}\mathrm{C}/\mathrm{O}\text{-}\mathrm{alkyl}\text{-}\mathrm{C} \mathrm{ratio}=\mathrm{Alkyl}\text{-}\mathrm{C}/\mathrm{O}\text{-}\mathrm{alkyl}\text{-}\mathrm{C}$$

(6)

$$OCA=(\mathrm{O}\text{-}\mathrm{alkyl}\text{-}\mathrm{C}+\mathrm{Carboxyl}\text{-}\mathrm{C})/(\mathrm{Aromatic}\text{-}\mathrm{C}+\mathrm{Alkyl}\text{-}\mathrm{C})$$

(7)

In the formula, O-alkyl-C represents alkoxy carbon; Alkyl-C represents alkyl carbon; Carboxyl-C represents carboxyl carbon; Aromatic-C represents aromatic carbon; and OCA represents the organic carbon lability index.

2.4. Statistical Analysis

All statistical analyses were performed using R software (version 4.4.3). One-way analysis of variance (one-way ANOVA) was employed to compare the effects of different exogenous carbon sources on soil organic carbon (SOC) content, nutrient levels, enzyme activities, organic carbon functional groups, and stability. Additionally, the least significant difference (LSD) test was used to determine significant differences (p < 0.05) between treatments. Pearson correlation analysis was conducted to examine the relationships between soil enzyme activities and SOC, soil nutrients, organic carbon functional group structures, and stability under exogenous carbon addition conditions. Principal component analysis (PCA) was performed using R version 4.4.3 with the “stats” and “FactoMineR” packages to analyze soil enzyme activities (represented by β-glucosidase, N-acetylglucosaminidase, cellulase, and alkaline phosphatase), soil nutrients (represented by ammonium nitrogen, nitrate nitrogen, available phosphorus, and available potassium), SOC, organic carbon chemical structure, and stability, aiming to identify the most effective exogenous carbon source. Furthermore, structural equation modeling (SEM) was implemented in R version 4.4.3 using the “lavaan” and “semPlot” packages to elucidate the pathways among soil nutrients, enzyme activities, organic carbon functional group structures, and SOC. The maximum likelihood method was used to model these variables, and the model fit was evaluated using the chi-squared (χ²) test and root mean square error of approximation (RMSEA). The graphs were created using Origin 2021 (9.8.0.200).

3. Results

3.1. Soil Organic Carbon

As shown in Figure 4, the soil organic carbon (SOC) content under T₁, T₂, and T₃ treatments was significantly higher than that under CK, with the values ranked in the order of T₁ > T₃ > T₂. Specifically, T₁ increased soil organic carbon content by 26.84% compared to CK.

3.2. Soil Nutrients

The effects of different exogenous carbon treatments on soil nutrient contents are shown in Figure 5. Compared to the T₀ treatment, soil ammonium nitrogen (NH₄⁺-N) content decreased under the T₃ treatment, while it significantly increased under CK, T₁, and T₂ treatments. The NH₄⁺-N content across exogenous carbon treatments followed the order T₁ > T₂ > T₃, with T1 increasing NH₄⁺-N content by 34.69% compared to CK. In contrast, soil nitrate nitrogen (NO₃⁻-N) content significantly decreased in all treatments relative to CK, with values ranked as T₂ > T₁ > T₃. Compared to CK, soil available phosphorus content significantly increased under T₁ by 21.61%, whereas it decreased under T₂ and T₃. Soil available potassium content showed no significant reduction under T₃ compared to CK, but it significantly decreased under T1 and T₂, with values ranked as T₃ > T₂ > T₁.

3.3. Soil Enzyme Activities

As shown in

Table 5. Effects of different exogenous carbon sources on soil enzyme activity.

Treatment	BG nmol/h/g	NAG umol/d/g	CBH μg /min /g	AKP mg/d/g
T0	3.82 ± 0.15c	13.90 ± 0.60c	38.68 ± 0.74a	467.71 ± 27.77c
CK	3.97 ± 0.21c	15.04 ± 0.19c	38.46 ± 0.20a	662.68 ± 13.61a
T1	5.37 ± 0.11a	9.17 ± 1.01d	34.39 ± 0.06b	650.10 ± 12.65a
T2	4.92 ± 0.14b	16.86 ± 0.41b	26.28 ± 2.42c	573.58 ± 17.94b
T3	4.01 ± 0.22c	19.68 ± 0.83a	34.67 ± 1.25b	603.98 ± 11.88ab

Different letters indicate significant differences in soil enzyme activity under different treatments (p < 0.05). BG—β-glucosidase; NAG—N-acetylglucosidase; CBH—cellulase; AKP—alkaline phosphatase.

, compared to the CK treatment, β-glucosidase (BG) activity significantly increased under all exogenous carbon treatments, with values ranked as T₁ > T₂ > T₃. Specifically, BG activity under T₁ increased by 35.26% compared to CK. In contrast, N-acetyl-β-glucosaminidase (NAG) activity significantly decreased under T₁ but increased under T₂ and T₃, with the highest activity observed in T₃, showing a 30.78% increase over CK. Cellobiohydrolase (CBH) activity significantly decreased across all exogenous carbon treatments compared to CK, ranking as T₃ > T₁ > T₂. Alkaline phosphatase (AKP) activity significantly decreased under T₂ relative to CK, while no significant differences were observed under T₁ and T₃.

3.4. Chemical Structural Characteristics and Stability of Soil Organic Carbon

As shown in the

Table 6. Effects of different exogenous carbon sources on relative peak areas of soil organic carbon functional groups.

Treatment	O-alkyl-C 1030 cm−1 (%)	Alkyl-C 1430 cm−1 (%)	Aromatic-C 1630 cm−1 (%)	Ketone-C 1798 cm−1 (%)	Carboxyl-C 2516 cm−1 (%)	Phenolic Alcohol 3620 cm−1 (%)
T0	72.28 ± 1.88a	26.74 ± 0.86c	0.44 ± 0.14b	0.13 ± 0.01c	0.26 ± 0.03b	0.16 ± 0.01d
CK	70.50 ± 1.23ab	28.17 ± 0.84bc	0.34 ± 0.12b	0.21 ± 0.02b	0.43 ± 0.07a	0.29 ± 0.04c
T1	68.29 ± 1.92b	30.33 ± 1.36a	0.41 ± 0.08b	0.28 ± 0.03a	0.28 ± 0.04b	0.41 ± 0.04b
T2	69.90 ± 1.77ab	28.99 ± 1.09ab	0.00 ± 0.00c	0.18 ± 0.03b	0.22 ± 0.04b	0.71 ± 0.09a
T3	68.73 ± 1.57b	29.74 ± 0.73ab	0.94 ± 0.25a	0.19 ± 0.00b	0.24 ± 0.06b	0.16 ± 0.03d

Different letters indicate significant differences in soil organic carbon functional groups under different treatments (p < 0.05). O-alkyl-C—alkoxy carbon; Alkyl-C—alkyl carbon; Aromatic-C—aromatic carbon; Ketone-C—ketone carbon; Carboxyl-C—carboxyl carbon.

, compared to the T₀ treatment, fertilization reduced the O-alkyl carbon proportion. Under different exogenous carbon inputs, the O-alkyl carbon proportions ranked as T₂ > T₃ > T₁, with no significant differences among the three. Compared to T₀, fertilization significantly increased the alkyl carbon proportion, with values under exogenous carbon treatments ranked as T₁ > T₃ > T₂. Specifically, T₁ increased the alkyl carbon proportion by 7.67% compared to CK. The aromatic carbon proportion significantly increased under T₃ by 176.47% compared to CK, while it was undetectable (0%) under T₂. Fertilization significantly elevated ketonic carbon proportions relative to T₀, with the highest value observed under T1, showing a 33.33% increase over CK. Exogenous carbon inputs significantly reduced carboxylic carbon proportions compared to CK, ranked as T₁ > T₃ > T₂. Additionally, phenolic compound proportions significantly increased under T₁ and T₂ compared to CK, with the highest value under T₂ (144.83% increase over CK).

As shown in Figure 6, compared to the T₀ treatment, the alkyl carbon/O-alkyl carbon ratio significantly increased under exogenous carbon treatments, with values ranked as T₁ > T₃ > T₂. Furthermore, the soil organic carbon lability index significantly decreased under exogenous carbon treatments compared to CK, ranked as T₁ < T₃ < T₂. Specifically, the soil organic carbon lability index under T₁ was reduced by 10.04% compared to CK.

As shown in the Figure 7, principal component 1 (PC1) and principal component 2 (PC2) accounted for 36.86% and 23.46% of the total variance in the PCA, respectively. In the total variation of organic carbon structural composition, along PC1, soil organic carbon structures under different exogenous carbon treatments exhibited highly significant differences (p = 0.0010 < 0.01), with non-overlapping clusters among treatments, where T1 showed the most pronounced effect. Along PC2, treatments also formed distinct clusters without overlap, with CK demonstrating the most significant separation.

3.5. Interrelationships Among Soil Enzyme Activities, Soil Nutrient Contents, and the Functional Group Structure of Soil Organic Carbon

As shown in Figure 8, β-glucosidase exhibited highly significant positive correlations with ammonium nitrogen (NH₄⁺-N), ketonic carbon, and phenolic compounds (p < 0.01), and significant positive correlations with available phosphorus, organic carbon, alkyl carbon, and the alkyl carbon/O-alkyl carbon ratio (p < 0.05). Conversely, it showed significant negative correlations with N-acetyl-β-glucosaminidase (NAG) and cellobiohydrolase (CBH) (p < 0.05). NAG demonstrated a significant positive correlation with available potassium (p < 0.05) but significant negative correlations with available phosphorus and ketonic carbon (p < 0.05). CBH exhibited highly significant negative correlations with organic carbon and phenolic compounds (p < 0.01). Alkaline phosphatase (AKP) showed significant positive correlations with nitrate nitrogen (NO₃⁻-N), available potassium, alkyl carbon, and the alkyl carbon/O-alkyl carbon ratio (p < 0.05), along with highly significant positive correlations with available phosphorus and ketonic carbon (p < 0.01), while displaying significant negative correlations with O-alkyl carbon and the soil organic carbon lability index (p < 0.05). Ammonium nitrogen (NH₄⁺-N) was significantly positively correlated with available phosphorus (p < 0.05) and highly significantly correlated with phenolic compounds (p < 0.01) but showed a highly significant negative correlation with aromatic carbon (p < 0.01). Available phosphorus exhibited a highly significant positive correlation with ketonic carbon (p < 0.01). Available potassium was significantly positively correlated with carboxylic carbon (p < 0.05). Organic carbon showed significant positive correlations with alkyl carbon and phenolic compounds (p < 0.05) but a significant negative correlation with carboxylic carbon (p < 0.05). O-alkyl carbon demonstrated highly significant negative correlations with alkyl carbon, ketonic carbon, and the alkyl carbon/O-alkyl carbon ratio (p < 0.01), while being highly significantly positively correlated with the soil organic carbon lability index (p < 0.01). Alkyl carbon displayed highly significant positive correlations with ketonic carbon and the alkyl carbon/O-alkyl carbon ratio (p < 0.01) but a highly significant negative correlation with the soil organic carbon lability index (p < 0.01).

3.6. Key Factors Influencing the Effects of Different Exogenous Carbon Sources on Soil Organic Carbon

Using structural equation modeling (SEM), this study elucidated the driving factors of soil enzymes, soil nutrients, and organic carbon chemical structure on soil organic carbon (SOC) content under different exogenous carbon additions. For fermented cattle manure as the exogenous carbon input (Figure 9a), SOC was directly influenced by β-glucosidase activity and indirectly by alkaline phosphatase activity; alkaline phosphatase affected SOC by mediating changes in available phosphorus. For corn straw as the exogenous carbon input (Figure 9b), SOC was both directly and indirectly influenced by β-glucosidase activity; the direct pathway involved β-glucosidase itself, while the indirect pathway occurred through its effects on phenolic compounds; additionally, N-acetyl-β-glucosaminidase (NAG) indirectly influenced SOC by altering alkyl carbon content. For the mixed input of fermented cattle manure and corn straw (Figure 9c), SOC was directly and indirectly influenced by NAG activity; the direct effect stemmed from NAG itself, while its indirect effect operated through aromatic carbon dynamics; β-glucosidase also indirectly affected SOC by mediating changes in alkyl carbon content.

4. Discussion

4.1. Effects of Different Exogenous Carbon Sources on Soil Organic Carbon and Nutrient Contents

Different exogenous carbon sources, due to their significant differences in physicochemical properties, lead to varying effects on soil organic carbon and nutrient content. Liu et al. [45] found that corn straw return significantly increased soil organic carbon content. The results of this study show that, compared to the CK treatment, the addition of exogenous carbon significantly (p < 0.05, Figure 4) increased soil organic carbon content. This aligns with the findings of Guo et al. [46] and Li et al. [47]. Specifically, the T₁, T₂, and T₃ treatments increased soil organic carbon content by 26.84%, 11.27%, and 18.63%, respectively. Among these, the T₁ treatment resulted in the highest soil organic carbon content, primarily due to the higher stability of soil organic carbon in T₁ (Figure 6), which facilitated the accumulation of soil organic carbon and thereby increased its content. Numerous studies [48,49] have demonstrated that corn straw return significantly enhances the content of soil ammonium nitrogen, available phosphorus, and available potassium. The results of this study indicate that, compared to the CK treatment, the T₁ treatment increased soil ammonium nitrogen and available phosphorus content by 34.69% and 21.61%, respectively. The T₂ treatment increased soil ammonium nitrogen content by 17.57%. Conversely, the T₃ treatment showed a reduction in soil nutrient content, likely attributable to the 50% reduction in conventional fertilizer application compared to the CK treatment.

4.2. Effects of Different Exogenous Carbon Sources on Soil Enzyme Activities

Soil enzymes are the most active components in the soil ecological environment, participating in all biochemical reactions in the soil [50]. Soil enzymes primarily originate from crop roots, crop residues, soil microorganisms, etc. [51]. They convert soil nutrients into forms readily absorbable and utilizable by crops [52], while regulating the cycling of nutrients such as carbon, nitrogen, and phosphorus [53,54]. Liu et al. [55] found that straw return significantly enhanced the activities of soil β-glucosidase (BG) and alkaline phosphatase in rainfed farmland on the Loess Plateau. The results of this study revealed that, compared to the CK treatment, the addition of different exogenous carbon sources significantly affected enzyme activity (p < 0.05, Table 5). Specifically, the T₁ treatment exhibited the highest β-glucosidase (BG) and alkaline phosphatase (AKP) activities, with BG activity increasing by 35.26% compared to CK. The T₃ treatment showed the highest N-acetyl-β-glucosaminidase (NAG) and cellobiohydrolase (CBH) activities, with NAG activity increasing by 30.78% compared to CK. Additionally, it was found that compared to the CK treatment, the NAG activity in the T₁ treatment decreased significantly (p < 0.05, Table 5). Similarly, the CBH and AKP activities in the T2 treatment decreased significantly (p < 0.05, Table 5). These findings contradict the results of Qiao et al. [56] and Lu et al. [57]. The primary reason may be the relatively low application rates of fermented cattle manure and corn straw in this study, which led to reduced soil enzyme activity. Conversely, the combined return of cattle manure and corn straw (T₃ treatment) significantly increased NAG, CBH, and AKP activities (p < 0.05, Table 5), indicating that the mixed application of different exogenous carbon sources is more conducive to enhancing soil enzyme activity.

4.3. Effects of Different Exogenous Carbons on the Chemical Structure and Stability of Soil Organic Carbon

The chemical structural characteristics of soil organic carbon are important indicators reflecting soil functionality and organic carbon stability [58]. Numerous studies [15,59,60] have demonstrated that differences in the content of functional-group organic carbon—such as O-alkyl carbon, alkyl carbon, aromatic carbon, and carboxyl carbon—in soil organic carbon alter its content and stability. Adding exogenous carbon is the most direct method to modify soil organic carbon content and chemical structural characteristics [16]. Due to the diversity and complexity of different exogenous carbon structures, their incorporation into fields leads to varying changes in the chemical structure and stability of soil organic carbon [15]. The results of this study show that all treatments with exogenous carbon addition exhibited the highest content of O-alkyl carbon, followed by alkyl carbon, consistent with the findings of Mahieu et al. [60] and Hao et al. [61]. The T₁ treatment showed the highest alkyl carbon and ketone carbon content, increasing by 7.67% and 33.33% compared to the CK treatment, respectively. This is primarily attributed to the highest relative content of alkyl carbon in fermented cattle manure. The T₃ treatment displayed the highest aromatic carbon content, increasing by 176.47% compared to CK. However, the aromatic carbon content in the T₂ treatment was zero, which contradicts the results of Nie et al. [62]. This discrepancy may arise because, under arid conditions on the Loess Plateau, corn straw return prompts microorganisms to preferentially utilize recalcitrant carbon sources to meet energy demands, leading to reduced aromatic carbon content. In contrast, combined application with decomposed cattle manure significantly mitigates this effect.

4.4. Driving Factors of Different Exogenous Carbons on Soil Organic Carbon

Soil organic carbon (SOC) can significantly enhance soil fertility and improve soil quality through sequestration [63]. Therefore, its content, structural characteristics, and stability serve as important indicators for evaluating soil quality [14]. Soil enzymes are key drivers of the SOC cycle, and the transformation of exogenous carbon into SOC involves a series of complex enzymatic reactions [64]. Thus, soil enzymes directly or indirectly influence SOC content and its chemical structural characteristics. This study found that under different exogenous carbon treatments, the SOC lability index showed a highly significant positive correlation with O-alkyl carbon (Figure 8, p < 0.01), while exhibiting highly significant negative correlations with alkyl carbon, ketone carbon, and the alkyl carbon/O-alkyl carbon ratio (Figure 8, p < 0.01). Furthermore, structural equation modeling (SEM) analysis revealed the following: under T₁ and T₂ treatments, β-glucosidase had a direct and highly significant positive effect on SOC (Figure 9a,b, p < 0.001). Under the T₃ treatment, N-acetylglucosaminidase had a direct and significant positive effect on SOC (Figure 9c, p < 0.01). Principal component analysis (PCA) results (Figure 7) demonstrated that among the T₁, T₂, and T₃ treatments, T₁ had the most significant and optimal impact on SOC content, structural characteristics, and stability, followed by T₃, while T₂ performed the worst.

5. Conclusions

Under the addition of different exogenous carbons in rainfed farmland, the T₁ treatment primarily relies on β-glucosidase as the main driver to directly influence soil organic carbon (SOC). Simultaneously, alkaline phosphatase (AKP) indirectly affects SOC by regulating soil available phosphorus. In the T₂ treatment, β-glucosidase serves as the principal driver to directly influence SOC, while also indirectly affecting SOC through phenolic–alcoholic compounds under the mediation of β-glucosidase. For the T₃ treatment, N-acetyl-β-glucosaminidase (NAG) acts as the main driver, directly or indirectly influencing SOC. Specifically, NAG indirectly impacts SOC through its effect on aromatic carbon. Through these mechanisms, the addition of different exogenous carbons significantly increased SOC content in farmland soil, with the order of SOC content being T₁ > T₃ > T₂. Notably, under the T₁ treatment, SOC content increased by 25.32–26.84% compared to CK (0–20 cm soil layer). Concurrently, the soil organic lability index decreased, with the order of indices being T₁ < T₃ < T₂. The T₁ treatment reduced the soil organic lability index by 10.04% compared to CK, thereby enhancing SOC stability. In summary, the incorporation of fermented cattle manure in rainfed farmland in the mountainous regions of southern Ningxia, which prioritizes driving phosphorus cycling and improving SOC content, is highly effective. From the perspective of agricultural circular development, the combined use of fermented cattle manure and straw-based carbon sources is recommended as a critical strategy and pathway for enhancing soil carbon sequestration, fertility, and green sustainable production in rainfed agricultural areas.