Diversified Crop Rotation Improves Soil Quality by Increasing Soil Organic Carbon in Long-Term Continuous Cotton Fields

Abstract

To explore the improvement effect of diversified crop rotation on soil quality in long-term continuous cotton fields (15 years), a field experiment was conducted in southern Xinjiang in 2024. With continuous cotton (C-C) as the control, four crop rotations, namely, cotton–maize (C-M), cotton–wheat (C-W), cotton–soybean (C-S), and cotton–peanut (C-P), were set up. The results showed that compared with C-C, the soil organic carbon (SOC) treated by C-P and C-S increased significantly by 11.76% and 3.38%, respectively, and the easily oxidized organic carbon (EOC) increased by 45.18% and 37.15%, respectively. The dissolved organic carbon (DOC) treated with C-S increased by 14.36%, while C-M decreased by 10.98%. The carbon pool index (CPI) of C-P was the highest in the 0–20 cm soil layer, which was 13.00% higher than that of C-C. The β-1, 4-glucosidase (BGL) activity of C-C at 0–20 cm and 20–40 cm was 144.70–387.26% and 48.01–71.32% higher than that of other treatments, respectively. The RuBisCo activity of C-P was 80.96% higher than that of C-C. The soil quality index was the highest for C-S, followed by C-P, which was 74.56% higher than that of C-C. In conclusion, the cotton–peanut rotation can effectively improve the soil quality of continuous cotton fields by increasing the organic carbon composition, enhancing the activity of carbon-fixing enzymes and bacterial diversity.

1. Introduction

Xinjiang is one of the earliest regions in China to start cotton cultivation and also the only long-staple cotton planting base in China [1]. Due to Xinjiang’s unique natural environmental conditions, this area is suitable for cotton cultivation. Therefore, since the 1980s, the area of cotton cultivation has been continuously expanding [1]. In terms of indicators such as planting area, total output, and average output, it has ranked among the top in the country for 24 consecutive years [2]. In addition, due to the monoculture of cotton in Xinjiang, especially in the main production areas, the area planted with cotton accounts for over 95% [3]. In traditional agricultural practices, continuous cropping (single-crop continuous cropping) usually leads to nutrient imbalance and land degradation, thereby limiting soil quality, causing environmental damage, and having a negative impact on the main functions of the soil [4]. In the cotton-growing areas of Xinjiang, after continuous cropping for more than 10 years, the soil organic carbon content will decrease by 12–15% [5]. Continuous cropping may reduce soil nutrients and increase harmful microorganisms [6,7]. This area has been engaged in large-scale continuous cotton cultivation for a long time, which not only reduces cotton production but also deteriorates the soil quality [2].

In recent years, the main research direction of cotton rotation in Xinjiang region focuses on the changes in certain elements in individual experiments [8,9], lacking a comprehensive evaluation of its soil quality. Crop rotation can enhance environmental sustainability, diversity of agricultural ecosystems, and soil quality [10]. Cotton–wheat rotation can increase soil organic carbon by 8–10% through straw returning to the field [11]. In sub-Saharan Africa and around the world, leguminous plant rotation systems have higher mineralized carbon and microaggregate carbon [12]. In the maize–soybean rotation system in Heilongjiang, China, carbon storage is closely related to topsoil quality but not to subsoil quality [13]. Strengthening crop diversity in crop rotation systems, especially the cultivation of leguminous crops, is regarded as an effective measure to promote sustainable agriculture, as it can promote soil health, ensure food security, improve the efficiency of agricultural ecosystems, and alleviate adverse ecological problems [11]. At present, the main focus in the Xinjiang region is on how to increase cotton yield through crop rotation while there are still deficiencies in soil quality assessment [2]. In order to enhance the ecological stability of the Xinjiang region, it is necessary to conduct a comprehensive study on the impact of crop rotation of major grain and oil crops in the Xinjiang region on soil quality. The SQI provides a simple indicator for evaluating the overall function of soil by aggregating multiple variables into one value, taking into account the trade-offs among ecosystem functions [14,15,16]. The SQI has been widely adopted and validated as an effective tool for assessing the impact of agricultural management practices on soil health across diverse cropping systems [17,18].

Rotating leguminous crops can effectively improve soil quality and reduce the harm of continuous cotton cropping. For this purpose, taking cotton-continuous cropping (C-C) as the control, the rotation of cotton to soybeans (C-S) and cotton to peanuts (C-P) were studied. To ensure national food security, Xinjiang is also gradually planting food crops on a large scale. Moreover, the first and second largest food crops in Xinjiang region are wheat and maize, respectively, accounting for one-third of the total crop output [19,20]. Meanwhile, crop rotation of wheat follows the principles of minimal soil disturbance, permanent soil cover, and diversified crop rotation, saving resources and making effective use of resources and thereby providing opportunities for energy conservation and greenhouse gas emission reduction [21]. An eight-year wheat–maize rotation study found that combined application of mineral fertilizers and organic enhancements could increase SOC [22]. Therefore, cotton rotation of wheat (C-W) and cotton rotation of maize (C-M) were also added. Given that the current four crop rotation models still have unclear effects on improving soil quality in continuous cotton fields in Xinjiang, a field experiment with cotton rotation as a control was carried out for verification. The aim is to explore the crop rotation model that has the most significant effect on improving soil quality in long-term continuous cotton fields.

2. Materials and Methods

2.1. Overview of the Experimental Zone

This research was conducted at the Teaching and Practice Center of Tarim University in the Alar Reclamation Area in 2024. The study area has a warm temperate continental arid desert climate, located at the upper reaches of the Tarim River and the northwest edge of the Taklmakan Desert. It is rich in light and heat resources, with intense surface evaporation and extremely scarce rainfall. The average annual temperature is 12.5 °C, the average annual solar radiation is 559.4–612.1 KJ/cm², the sunshine duration is 2556.3–2991.8 h, the annual active accumulated temperature reaches 4721.2 °C, the frost-free period is 180–224 d, and the average annual evaporation is 1976.62–5589.9 mm [23]. The average annual precipitation is 23.7 to 82.5 mm, and the groundwater depth is less than 3 m. It is a typical irrigated agricultural area. The soil type of the test site is sandy loam.

2.2. Experimental Design

This experiment was a randomized block experiment. On a cotton field with continuous cropping for 15 years, based on this, four rotation modes were set up, namely, C-S, C-P, C-W, and C-M, with C-C as the control (

Table 1. Schematic of rotation cropping patterns.

Rotation Crop	Variety (Key Trait Indication)	Planting Pattern (Technical Features)	Planting Density (Unit and Value)
Cotton	Tahe 2	One-film six-row	26 × 104 plants·hm−2
Soybean	Xindadou 27	One-film six-row	27.2 × 104 plants·hm−2
Peanut	Huayu 25	One-film three-row two-belt	18.0 × 104 plants·hm−2
Spring Wheat	Xinchun 22	Drill seeding	450,000 grains·hm−2
Spring Maize	Denghai 618	One-film four-row	108,000 plants·hm−2

). Each planting pattern is repeated three times. The area of the plots is 13.5 m × 60 m, which is a total of 810 m². The walkway between plots is 40 cm, and the walkway between repetitions is also 60 cm. A 1-m protective row is set around the perimeter.

2.3. Sampling

After the cotton harvest in October 2024, soil samples from the 0–20 cm and 20–40 cm layers were collected from each plot using the five-point sampling method. The range of 0–20 cm is a typical plough layer, which is the area with the densest crop roots, the most direct impact of agricultural operations (such as ploughing and fertilization), and the most concentrated input of organic materials (such as crop residues). Therefore, the expected improvement effect of any crop rotation measures on soil properties is most significant at this layer [24]. The 20–40 cm layer belongs to the subsurface layer and is less affected by surface management measures, but its nature is crucial for the deep supply of water and nutrients to crops. By comparing these two levels, the depth and persistence of the impact of crop rotation measures can be evaluated [25]. After removing gravel and plant residues, the soil was thoroughly mixed. The fresh soil was passed through a 2 mm sieve and divided into three portions: one portion was used for the determination of dissolved organic carbon (DOC), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and enzyme activities. Another portion was air-dried for the measurement of soil organic carbon (SOC), soil easily oxidized organic carbon (EOC), and Refractory organic carbon (ROC) contents. And the remaining fresh soil was stored in an ultra-low temperature freezer at −80 °C for the analysis of soil bacteria and fungi.

2.4. Determination Items and Methods

2.4.1. Determination of Soil Organic Carbon and Its Active Components

Measure the soil layers of 0–20 cm and 20–40 cm before sowing and after harvest.

Determination of SOC by the Potassium Dichromate External Heating Method [26]:

$$\mathrm{SOC}(\mathrm{g}/\mathrm{k}\mathrm{g})={\displaystyle \frac{\frac{\mathrm{c}\times 5}{{\mathrm{V}}_0}\times ({\mathrm{V}}_0-\mathrm{V})\times {10}^{-3}\times 3.0\times 1.1}{\mathrm{m}\times \mathrm{k}}}\times 1000$$

(1)

where c: concentration of the 0.8 mol/L (1/6 K₂Cr₂O₇) standard solution, 5: volume of potassium dichromate standard solution added (mL), V₀: volume of FeSO₄ consumed in blank titration (mL), V: volume of FeSO₄ consumed in sample titration (mL), 3.0: molar mass of 1/4 carbon atom (g/mol), 10⁻³: conversion factor from milliliters to liters, 1.1: oxidation correction coefficient, m: mass of air-dried soil sample (g), and k: conversion coefficient for converting air-dried soil to oven-dried soil.

Determination of Dissolved Organic Carbon by Cold Water Extraction Method [26]: We performed the following steps: Weigh 15 g of fresh soil sieved through a 1 mm mesh, add 30 mL of distilled water, shake at room temperature for 30 min, centrifuge at 4000 r/min for 15 min, filter the supernatant through a 0.45 μm membrane, add 5 mL of 0.8 mol/L K₂Cr₂O₇ solution and 5 mL of concentrated sulfuric acid to the filtrate, digest at 185 °C for 5 min, and titrate with 0.2 mol/L FeSO₄.

Determination of Soil Easily Oxidizable Organic Carbon by Potassium Permanganate Oxidation Method [26]: We performed the following steps: Prepare stock solutions of 0.02 mol/L KMnO₄ and 0.1 mol·L⁻¹ CaCl₂ (6.32 g KMnO₄ and 29.40 g CaCl₂ diluted to 2 L with deionized water), and 0–0.02 mol/L KMnO₄ standards. Add 5 g of soil sample to a 50 mL plastic centrifuge tube, add 20 mL of KMnO₄-CaCl₂ solution, then place the sample on a reciprocating shaker and shake for 2 min (180 revolutions/min), followed by standing for 10 min. Take 200 μL of the supernatant, dilute to 10 mL, and measure the absorbance at 550 nm using a spectrophotometer.

$$EOC(\mathrm{m}\mathrm{g}/\mathrm{k}\mathrm{g})=[0.02-(a+bz\left)\right]\times 9\times (0.025/\mathrm{m})$$

(2)

where 0.02: initial concentration of KMnO₄ (mol/L); a: intercept of the standard curve; b: slope of the standard curve; z: absorbance of the sample; 9: amount of carbon oxidized by 1 mol of MnO₄⁻ from Mn⁷⁺ to Mn²⁺ [g C (0.75 mol)/mol]; 0.025: volume of KMnO₄ (L); m: sample mass (g) [26].

Refractory organic carbon (ROC) = SOC − EOC

(3)

Determination of Soil Microbial Biomass Carbon and Soil Microbial Biomass Nitrogen by Chloroform Fumigation-K₂SO₄ Extraction Method [26]: The difference between the organic carbon/nitrogen content of fumigated soil and non-fumigated soil is used for conversion, as follows:

$$MBC/MBN(mg/kg)=Ec/kec$$

(4)

where Ec is the difference in organic carbon/nitrogen content between fumigated and non-fumigated soil. kec is the conversion coefficient, with a value of 2.22.

2.4.2. Key Enzyme Activities for Soil Carbon Sequestration

The activity of soil β-1,4-glucosidase was determined by the nitrophenol colorimetric method [27]. The principle is to use p-nitrobenzene-β-D-pyranoglucoside as the matrix, hydrolyze to produce p-nitrophenol, and define its activity (µgPEP/g/h) by the content of p-nitrophenol produced in 1 g of soil within 1 h.

The activities of soil cellulase, sucrase, and amylase were all determined by the 3, 5-dinitrosalicylic acid method [28]. Among them, soil cellulase activity was defined as the glucose content produced by 1 g of soil within 72 h (mg glucose/g/72 h); soil sucrase activity was defined as the glucose content produced by 1 g of soil within 24 h (mg Glucose/g/24 h); and soil amylase activity was defined as the glucose content produced by 1 g of soil within 24 h (mg Glucose/g/24 h).

Determination of ribulose 1, 5-diphosphate carboxylase content [29]: The determination was carried out using the kit from Aoqing (Nanjing) Biotechnology Co., Ltd. (Nanjing, China). For detailed determination methods, please refer to the instructions in the corresponding kit. The absorbance value was measured at a wavelength of 340 mm with a microplate reader. Steps: It is recommended to weigh approximately 0.1 g of the sample, add 1 mL of extract One, homogenize it in an ice bath, and then ultrasonically break it (ice bath, 200 W, break for 3 s, intermittent for 7 s, total time for 1 min). After that, centrifuge at 8000× g at 4 °C for 10 min and take the supernatant for determination.

2.4.3. Alpha Diversity of Soil Bacteria and Alpha Diversity of Soil Fungi

Microbial DNA was extracted from soil samples using the E.Z.N.A.^® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s protocols. The V4-V5 region of the bacteria 16S ribosomal RNA gene was amplified by PCR (95 °C for 2 min, followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s and a final extension at 72 °C for 5 min) using primers 515F5′-barcode-GTGCCAGCMGCCGCGG-3′and907R5′-CCGTCAATTCMTTTRAGTTT-3′, where the barcode is an eight-base sequence unique to each sample [30]. PCR reactions were performed in triplicate 20 μL mixture containing 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions.

Microbial DNA was extracted from soil samples using the E.Z.N.A.^® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s protocols. The fungal ITS genes were amplified by PCR (95 °C for 2 min, followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s and a final extension at 72 °C for 5 min) using primers ITS1F5′-barcode-CTTGGTCATTTAGAGGAAGTAA-3′ and ITS2R 5′-GCTGCGTTCTTCATCGATGC-3′ [31], where the barcode is an eight-base sequence unique to each sample. PCR reactions were performed in triplicate 20 μL mixture containing 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions.

2.4.4. Calculate Soil Quality Index

The evaluation of the soil quality index estimates an SQI based on the total data set method. Soil properties: Soil organic carbon components and potential enzyme activity have been suggested by some authors as useful soil quality indicators. All soil indicators were standardized to obtain dimensionless scores ranging from 0 to 1, thereby determining the soil quality index under different fertilization strategies:

$$\mathrm{SL}={\displaystyle \frac{\mathrm{x}-\mathrm{L}}{\mathrm{H}-\mathrm{L}}}$$

(5)

Here, x represents the soil index value, H and L represent the highest and lowest values, respectively, and SL represents the linear score (0–1). Based on the area of the radar chart, calculate the SQI score using the SQI area method:

$$\mathrm{SQ}{\mathrm{I}}_{\text{-}\mathrm{area}}=0.5\times {\sum }_1^{\mathrm{n}}\times {\mathrm{S}}_{\mathrm{L}}^2\sin {\displaystyle \frac{2\mathsf{\Pi }}{\mathrm{n}}}$$

(6)

where n and SQI area represent the quantity of soil properties and the SQI score, respectively [32].

2.4.5. Calculation Indicators

$$\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n} \mathrm{p}\mathrm{o}\mathrm{o}\mathrm{l} \mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y}\left(\mathrm{L}\right)=\mathrm{R}\mathrm{O}\mathrm{C}/(\mathrm{S}\mathrm{O}\mathrm{C}-\mathrm{R}\mathrm{O}\mathrm{C})$$

(7)

$$\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n} \mathrm{p}\mathrm{o}\mathrm{o}\mathrm{l} \mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\left(\mathrm{L}\mathrm{I}\right)=\mathrm{L}\mathrm{t}/\mathrm{L}\mathrm{c}\mathrm{k}$$

(8)

$$\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n} \mathrm{p}\mathrm{o}\mathrm{o}\mathrm{l} \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\left(\mathrm{C}\mathrm{P}\mathrm{I}\right)=\mathrm{S}\mathrm{O}\mathrm{C}\mathrm{t}/\mathrm{S}\mathrm{O}\mathrm{C}\mathrm{c}\mathrm{k}$$

(9)

$$\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{b}\mathrm{o}\mathrm{n} \mathrm{P}\mathrm{o}\mathrm{o}\mathrm{l} \mathrm{M}\mathrm{a}\mathrm{n}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}\left(\mathrm{C}\mathrm{M}\mathrm{P}\mathrm{I}\right)=\mathrm{C}\mathrm{P}\mathrm{I}\times \mathrm{L}\mathrm{I}\times 100$$

(10)

In the formula, Lt represents the carbon pool activity of each treatment. Lck is used as a reference for carbon pool activity (the study is compared with continuous cropping of cotton); SOCt and SOCck are the soil organic carbon contents of each treatment and the control, respectively, with the unit of g·kg⁻¹ [33].

2.5. Data Analysis

First, we summarized the data. One-way analysis of variance (ANOVA) test was conducted using IBM SPSS Statistics 27 to test and compare the mean values. The data collected from the field experiments were statistically analyzed, and Tukey’s test was used to compare the significance of the differences (p ≤ 0.05). They were plotted with Origin 2021.

3. Results

3.1. Soil Organic Carbon and Its Pool Components

The research found that in the 0–20 cm soil layer (Figure 1), diversified rotation significantly affected the soil carbon composition. Compared with C-C, the SOC of cotton–legume rotation (C-P and C-S) significantly increased by 11.76% and 3.38%, respectively, and the increase was more obvious than that of cotton–grain rotation (C-W; C-M). The EOC of cotton–legume rotation increased by 45.16% and 37.14% compared with C-C, respectively. The DOC of C-S significantly increased by 14.36%, while that of C-M decreased by 10.98%, relative to C-C. The SOC and carbon components in the 20–40 cm soil layer are not significantly affected by the crop rotation mode. Therefore, diversified crop rotation has a regulatory effect on the carbon pool of the surface soil (0–20 cm) but has no significant impact on the deep soil (20–40 cm).

3.2. Soil Carbon Pool Index

By calculating the CPI, L, LI, and CMPI, it can be found that in the 0–20 cm soil layer (

Table 2. Carbon pool indicators in different crop rotation modes; different letters within the same soil layer indicate significant differences among different crop rotation patterns (p < 0.05).

Treatment		CPI	L	LI	CMPI
0–20	C-C	1.00 ± 0.01 c	0.12 ± 0.002 c	1.00 ± 0.01 c	100.00 ± 1.36 d
	C-W	0.98 ± 0.01 d	0.13 ± 0.001 b	1.10 ± 0.01 b	107.96 ± 0.97 c
	C-M	0.91 ± 0.01 e	0.12 ± 0.001 c	1.03 ± 0.01 c	93.58 ± 0.36 e
	C-P	1.13 ± 0.01 a	0.15 ± 0.002 a	1.27 ± 0.01 a	143.30 ± 1.48 a
	C-S	1.03 ± 0.01 b	0.15 ± 0.004 a	1.28 ± 0.04 a	131.35 ± 3.82 b
20–40	C-C	1.00 ± 0.01 c	0.10 ± 0.001 d	1.00 ± 0.01 d	100.00 ± 0.83 e
	C-W	0.99 ± 0.01 d	0.11 ± 0.002 c	1.13 ± 0.02 c	111.37 ± 2.15 c
	C-M	1.04 ± 0.01 a	0.10 ± 0.003 d	1.03 ± 0.03 d	107.08 ± 2.89 d
	C-P	0.99 ± 0.01 d	0.14 ± 0.001 b	1.46 ± 0.01 b	144.77 ± 1.06 b
	C-S	1.03 ± 0.01 b	0.15 ± 0.001 a	1.55 ± 0.01 a	160.24 ± 1.18 a

Note: C-C: continuous cropping of cotton; C-W: cotton rotation with wheat; C-M: cotton rotation with maize; C-P: cotton rotation with peanuts; C-S: cotton rotation with soybeans; CPI: carbon pool index; L: carbon pool activity; LI: carbon pool activity index; CMPI: carbon pool management index.

), the CPI of C-P is the highest, which is significantly 12.60%, 14.41%, 23.60%, and 9.52% higher than that of C-C, C-W, C-M, and C-S, respectively. A significantly higher L value, greater by 27.31%, 16.08%, and 23.92%, was observed under C-P and C-S than under C-C, C-W, and C-M, respectively. The LI of C-C was 8.83%, 2.67%, 21.43%, and 21.72% lower than that of C-W, C-M, C-P, and C-S, respectively. The CMPI of C-M was significantly lower than that of C-C, C-W, C-P, and C-S by 6.42%, 13.32%, 34.70%, and 28.76%, respectively.

In the 20–40 cm soil layer, the CPI under C-M was significantly higher than that under C-C, C-W, C-P, and C-S by 4.41%, 5.55%, 5.15%, and 1.16%, respectively. The value of L under C-C and C-M was significantly lower than that under C-W, C-P, and C-S by 11.13%, 31.36%, and 35.58%, respectively. In contrast, the elevation in the LI value for C-S was also significant relative to C-C, C-W, C-M, and C-P, with increases of 55.25%, 37.89%, 51.37%, and 6.48%, respectively. Similarly, the CMPI under C-S exceeded that under C-C, C-W, C-M, and C-P by 60.24%, 43.88%, 49.65%, and 10.69%, respectively. Overall, cotton–grain rotations (C-W and C-M) exhibited lower carbon pool activity and management indices, suggesting that diversified rotation systems—particularly cotton–legume rotations—play a significant role in optimizing the soil carbon pool.

3.3. Soil Enzyme Activity

The research found that after diversified crop rotation in long-term continuous cotton fields, there were significant differences in soil enzyme activities: In the 0–20 cm soil layer (Figure 2), C-C exhibited a significantly greater BGL—150.23%, 144.70%, 387.26%, and 145.89% higher than C-W, C-M, C-P, and C-S, respectively. The SUV under C-P was 37.02% lower than under C-C. Compared to C-M and C-P, C-S had a significantly lower CL by 50.83% and 48.73%, respectively. The AMS of C-P was significantly lower than that of C-W, C-M, and C-S by 47.61%, 48.12%, and 48.01%, respectively.

In the 20–40 cm soil layer (Figure 2), the BGL in C-C remained significantly elevated, exceeding that in C-W, C-M, C-P, and C-S by 59.28%, 48.01%, 66.01%, and 71.32%, respectively. Furthermore, C-C exhibited a 35.73% higher SUV than C-P. Conversely, the CL was markedly lower in C-S, showing reductions of 21.91%, 38.29%, 38.27%, and 25.47%, compared to C-C, C-W, C-M, and C-P. The RuBisCo content under C-P was considerably greater, surpassing levels in C-C, C-W, C-M, and C-S by 36.78%, 74.36%, 44.52%, and 201.06%, respectively. For AMS, the C-W and C-M levels averaged 35.24% above C-C, while those in C-P and C-S averaged 74.43% below.

Therefore, C-C has a significant advantage in BGL and SUV activities, while C-P significantly increases RuBisCo activity but significantly inhibits AMS activity. The overall enzyme activity of cotton–grain rotation is relatively low, and the CL activity of C-S is weak in deep soil.

3.4. Microbial Diversity

As shown in Figure 3, the analysis of soil bacterial community abundance and diversity reveals that in the 0–20 cm layer, the C-S rotation had the lowest values for the Chao1, ACE, Shannon, and Simpson indices. Compared to C-S, the C-P treatment showed significant increases in the Chao1 (11.17%), ACE (11.47%), Shannon (2.25%), and Simpson (0.05%) indices. Relative to C-S, the indices Chao1, ACE, Shannon, and Simpson were also markedly higher under C-W, rising by 10.07%, 11.14%, 2.41%, and 0.04%, respectively. In the 20–40 cm soil layer, compared to C-C, C-M demonstrated significant elevations in the Chao1, ACE, Shannon, and Simpson indices by 13.89%, 16.16%, 2.70%, and 0.05%, respectively.

As shown in Figure 4, the analysis of fungal community abundance and diversity in rotational soils revealed that in the 0–20 cm layer, both C-C and C-M exhibited the highest values for the ACE, Shannon, and Simpson indices. Additionally, the Chao1 index under C-C was significantly higher (9.50%) than under C-M. In the 20–40 cm soil layer, the Chao1 index under C-C significantly exceeded that of C-W, C-M, C-P, and C-S by 19.49%, 18.56%, 33.46%, and 47.49%, respectively. Similarly, the ACE index was highest in C-C, surpassing C-W, C-M, C-P, and C-S by 16.46%, 23.10%, 34.58%, and 7.56%. The Shannon index also showed significantly higher values in C-C by 14.16%, 10.20%, 5.34%, and 14.81% compared to C-W, C-M, C-P, and C-S. For the Simpson index, C-C outperformed C-W, C-M, and C-S by 2.96%, 3.11%, and 3.35%, while C-P also exceeded these three treatments by 1.78%, 1.92%, and 2.16%, respectively.

3.5. Soil Microbial Biomass

As shown in Figure 5, the MBC of C-P in the 0–20 cm soil layer was significantly 11.96% higher than that of C-M, and the MBN of C-P was significantly increased by 31.01%, 40.71%, and 29.12% compared with C-C, C-M, and C-S, respectively. In the 20–40 cm soil layer, the diversified rotation has a relatively small disturbance to MBC and MBN, and there is no significant difference among the various treatments.

3.6. Soil Quality Index

After calculating the SQI of different soil layers, it can be observed in Figure 6 that there are significant differences among the treatments in the 0–20 cm soil layer. For example, the SQI of cotton–legume rotation were significantly 74.56% and 11.77% higher than C-C, respectively, while those of cotton–grain rotation were significantly 22.11% and 39.86% lower than those of C-C. In Figure 6d, it is clearly observed that the differences in soil quality index among various treatments in the 20–40 cm soil layer are very small or have no significant differences.

3.7. Correlation Analysis

Through correlation analysis (Figure 7), it was found that in the soil layer of 0–20 cm, bacterial α-diversity (B-α) was significantly negatively correlated with RuBisCo, while it was significantly positively correlated with CL. Fungal α-diversity (F-α) was significantly negatively correlated with SQI, organic carbon components, and BGL and significantly positively correlated with SUV. SQI mainly has a significant positive correlation with SOC, EOC, DOC, ROC, MBC, MBN, and AMS. In the soil layer of 20–40 cm, F-α has a significant negative correlation with MBC and MBN while having a significant positive correlation with ROC. SQI has a significant positive correlation with SOC, AMS, CL, and RuBisCo.

4. Discussion

4.1. Enhancement of Soil Organic Carbon Components by Diversified Crop Rotation

Continuous cropping in Xinjiang cotton-growing areas for more than 10 years will lead to a 12–15% decrease in SOC content [11], while cotton–wheat rotation can increase SOC by 8–10% through straw returning to the field [6]. In this study, the carbon pool enhancement effect of cotton–legume rotation was superior to that of cotton–grain rotation, which is consistent with the conclusion that cotton–soybean rotation enhances soil carbon pool activity in the Guanzhong area of Shanxi Province. Continuous cropping of soybeans often leads to soil diseases, soil acidification, reduced soil fertility, increased plant diseases, and decreased soybean yield, seriously threatening national food productivity and ecological sustainability [6]. Strengthening crop diversity in rotation systems, especially by incorporating legumes, is regarded as an effective measure to promote sustainable agriculture. This is because it can promote soil health, ensure food security, improve the efficiency of agricultural ecosystems, and alleviate adverse ecological problems [34]. In this study, the positive regulation of the carbon pool by cotton-legume rotation (especially C-P) is consistent with the mechanism of nitrogen fixation and increased root carbon input in leguminous crops.

In this study, the cotton–legume rotation (especially C-P and C-S) significantly increased SOC and its active components, which had particularly important ecological significance under the background of sandy loam in the experimental field. Sandy loam, because of its high content of sand, usually shows a lower primary cation exchange capacity (CEC) and weak aggregate structure, making it inherently fertilizer-protecting and more likely to suffer from poor water retention capability and erosion [35]. In such soil, the accumulation of SOC is no longer a simple “quantitative change” but a crucial “qualitative change” process. SOC is usually highly positively correlated with CEC because soil organic matter itself carries a large amount of negative charge, which can greatly enhance the soil’s adsorption capacity for nutrient cations (such as NH⁴⁺, K⁺, and Ca²⁺), thereby compensating for the inherent fertility deficiency of sandy soil [36]. Meanwhile, SOC, as the core “cementing agent” for the formation of soil aggregates, can minimize the soil’s erodibility. By promoting the formation of stable aggregates, it effectively resists wind erosion and water erosion, which is of vital importance for the Xinjiang Alar Reclamation Area located in an arid and sandy region [37,38]. Therefore, the increase in SOC observed in this study, especially the 11.76% rise under the C-P, indicates that this rotation model not only increased the soil’s carbon pool but also likely fundamentally improved the physical structure and chemical fertility function of this fragile soil type, laying a solid foundation for sustainable production.

In the 0–20 cm soil layer, the MBC and MBN of C-P were significantly 11.96% and 40.71% higher than those of C-M, respectively. There was no significant difference in microbial biomass in deep soil. The CMPI shows that the CMPI of cotton–legume rotation in surface soil (143.30 ± 1.48) is significantly higher than that of cotton–grain rotation (93.58 ± 0.36). The rotation of cotton beans can increase soil MBC by 20–30%, which is consistent with the increase in MBC in C-S in this study. This study confirmed that cotton-legume rotation (especially C-P) significantly enhanced the stability of the surface soil carbon pool (CMPI increased by 43.3%) through biological nitrogen fixation of leguminous crops and carbon input from straw, which is consistent with the mechanism of “nitrogen promoting carbon” in leguminous and poaceae rotation at home and abroad [39]. The CMPI of cotton–grain rotation decreased, which might be related to the high carbon/nitrogen ratio (C/N) of wheat and maize stalks, the slow decomposition rate, and the difficulty in supplementing soil carbon in the short term [40]. There was no significant change in the carbon composition of deep soil, suggesting that the carbon sequestration effect of crop rotation was mainly concentrated in the topsoil with active root systems, which was consistent with the characteristics of shallow-root crops dominating carbon input in Xinjiang cotton-growing areas [41].

Unlike the surface soil, all crop rotation treatments had no significant impact on SOC and its active components (EOC; DOC) in the 20–40 cm soil layer. This confirms that the direct impact of crop rotation on carbon input is limited in depth, and fresh organic matter is difficult to migrate downward rapidly and convert into deep soil organic carbon [42]. Although the total amount of carbon components remained unchanged, the CMPI showed significant changes in deep soil; especially the CMPI of C-S was even higher than that of all other treatments (Table 2). This indicates that leguminous crops may have directly introduced higher-quality organic matter into the deep soil through the death and decomposition of their deeper main root systems [43], or their root secretions may have stimulated the “activation effect” of deep microorganisms on the original stubborn carbon [44], thereby optimizing the activity and quality of the deep carbon pool rather than its total amount. This is a discovery far more significant than “the carbon content remains unchanged”.

4.2. Diversified Crop Rotation Patterns Have Increased the Activity of Soil Enzymes

The activities of BGL and SUV in C-C were significantly higher than those in rotation treatment. This study found that the activity of BGL in the soil of long-term continuous cropping cotton fields was significantly higher than that of all crop rotation treatments. BGL is a key enzyme in cellulose decomposition, and its high activity is often regarded as a sign of an active carbon cycle. However, in the context of long-term continuous cropping, this abnormally high activity is more likely to reflect the functional imbalance of the microbial community caused by the input of a single carbon source (cotton residues). It indicates a tendency to quickly mine and dominate the carbon short-circuit loop way, namely, the fresh plant residues and soil organic carbon was quickly broken down to the original CO₂ rather than into the stable soil organic matter [45,46]. This inference is consistent with the phenomenon observed in this study that the fungal diversity in continuous cotton fields is the highest, as fungi are usually the main drivers of litter decomposition [47]. On the contrary, crop rotation systems (especially C-P) reduce the activity of BGL by introducing diverse carbon inputs and direct the carbon flow more towards biocarbon fixation pathways represented by RuBisCo [48]. Therefore, the mechanism by which crop rotation improves the soil carbon pool lies not only in increasing carbon input but also in reshaping the micro-path of the carbon cycle, transforming from the “high decomposition and low sequestration” model under continuous cropping to a healthier model of “balanced decomposition and sequestration” [49].

The activity of RuBisCo of C-P was significantly increased, but the AMS was decreased. Due to the long-term input of a single carbon source, continuous cropping cotton fields selectively enrich enzymes that decompose simple carbon compounds (such as SUV), while crop rotation can promote the activity of complex carbon-decomposing enzymes (such as AMS) [50,51]. The increase in RuBisCo activity of C-P indicates that the photosynthetic carbon distribution of peanut roots is enhanced, promoting soil carbon fixation. However, its AMS activity decreased, which might be related to the high lignin content and slow decomposition of peanut straw [52]. Maize–soybean rotation can increase soil cellulase activity by 25% [53], which is different from the result of no significant increase in AMS activity of C-S in this study. It may be related to the expression of soil water-limiting enzyme activity in the arid area of Xinjiang [54].

In the 20–40 cm soil layer, the C-P still exhibited the highest RuBisCo activity (Figure 2). In such an environment with scarce light and relatively limited carbon sources, the enhanced activity of RuBisCo strongly suggests the presence of certain autotrophic or chemoautotrophic microorganisms. These microorganisms can fix CO₂ or utilize other inorganic energy sources, which may be a long-overlooked key mechanism for maintaining or even enhancing deep soil carbon sequestration [55]. Consistent with the surface soil, the C-C still maintained the highest BGL activity in the deep soil. This indicates that the microbial functional characteristics characterized by rapid decomposition shaped by long-term continuous cropping have permeated deeper soil layers, creating a deep soil environment that is not conducive to carbon storage [56,57].

4.3. The Diversified Rotation Model Has Increased the Diversity of Soil Microorganisms

In the 0–20 cm soil layer, the bacterial α-diversity of C-S is the lowest, while that of C-P and C-W is relatively high. Fungal α-diversity is higher in C-C and C-M. The SQI shows that the SQI of cotton–legume rotation is increased by 74.56% (C-P) and 11.77% (C-S) compared with continuous rotation, while it is decreased by 22.11% (C-W). Continuous cropping in Xinjiang cotton-growing areas leads to a single microbial community structure, while crop rotation can restore microbial diversity [54], which is consistent with the results of an increased bacterial Chao1 index in cotton crop rotation in this study. Crop rotation can promote the increase in soil bacterial community diversity by changing the composition of root secretions [58], which is consistent with the increase in the Chao1 index of C-P in this study. However, in this study, the bacterial diversity of C-S decreased, which might be related to the convergent evolution of rhizosphere microorganisms caused by continuous cropping of soybeans [59].

In the 20–40 cm soil layer, the bacterial α diversity of C-M was the highest (Figure 3). As a deep-rooted grass crop, maize’s vast root system may have created more diverse microhabitats and carbon sources for microorganisms in deep soil, thereby significantly enhancing the diversity of bacteria [60]. This contrasts with the mediocre performance of C-M in the surface soil, revealing the differentiated effects of different crops on specific soil layers. An alarming finding is that C-C still maintained the highest fungal alpha diversity in deep soil (Figure 4). Given that the major soil-borne diseases of cotton (such as wilt and damping-off) are mostly caused by fungal pathogens, this discovery implies that the problem of fungal community imbalance (which may accumulate pathogenic bacteria) caused by continuous cropping is not limited to the plough layer but has already endangered the deep soil ecosystem, which may increase the risk of crop diseases [61].

Six-year crop rotation increased fungal abundance by 42.7–69.2% but reduced the ratio of soil bacteria to fungi and fungal diversity [62]. In this study, the fungal diversity of continuous cropping C-C was higher than that of rotation treatment, which might be related to the stronger adaptability of fungi in arid areas to continuous cropping stress [63]. In the arid areas of Xinjiang, cotton–legume rotation can improve soil quality more effectively than cotton–grain rotation, which is closely related to the drought tolerance and rhizosphere growth promotion of leguminous crops. For instance, the SQI of C-P was 74.56% higher than that of C-C, indicating that peanut rotation can simultaneously achieve carbon sequestration and soil health improvement under water-saving irrigation conditions [64]. However, this study is only based on one-year experimental data. The cumulative effects of long-term crop rotation (such as 3–5 years) on deep soil carbon pools and microbial communities still need further verification. Furthermore, the deep CMPI of C-S in cotton crop rotation is superior to that of C-P, suggesting the potential advantage of soybeans in carbon sequestration–deep soil improvement, and it can be used as an alternative model for sustainable rotation in cotton fields in arid areas.

This study found that, compared with the 0–20 cm soil layer, the SQI of all treatments in the 20–40 cm soil layer was at a relatively low level, and there is no significant difference among C-M, C-P and C-S (Figure 6d). This result clearly indicates that the core effect of a one-year short-term crop rotation in improving soil quality is mainly concentrated in the plough layer. This is mainly determined by the following mechanism and is consistent with previous studies: crop residues, root secretions, and root exfoliation are the main sources of soil organic carbon and nutrients. The vast majority of these organic inputs are concentrated in the plough layer soil of 0–20 cm. Although deep-rooted crops (such as leguminous plants) can transfer some carbon to the deep layer, their absolute flux is much lower than that of the surface layer [42]. Therefore, the fresh carbon input brought by crop rotation is difficult to significantly change the organic carbon pool basis of deep soil in the short term, and SOC is the core indicator for calculating SQI. The activities of soil microorganisms strongly depend on the input of fresh organic matter. In the 20–40 cm soil layer, carbon sources and energy are relatively scarce, resulting in the scale and activity of the entire microbial community being lower than those in the surface layer [42,65]. The results of this study show that there is no significant difference in MBC and MBN in this soil layer (Figure 5), and the activities of most carbon cycle enzymes are relatively low. These factors jointly lead to the slow biogeochemical processes in deep soil, making it difficult to significantly improve the SQI in the short term through crop rotation measures. Conventional agricultural management measures, such as fertilization, irrigation, and straw returning to the field, also have a direct impact mainly concentrated in the plough layer [55,66]. Although water and soluble nutrients will leach downward, their shaping effect on soil structure and biological properties is far less intense than that on the surface soil. However, “no significant difference” does not mean “no impact”—in-depth analysis reveals potential positive signals: Although there was no overall difference in SQI, this study found that in the 20–40 cm soil layer, the CMPI of the C-S was significantly the highest (Table 2), while the RuBisCo activity of the C-P was significantly enhanced (Figure 2). These two key findings suggest that leguminous crops may have directly introduced organic matter into the subsurface layer through their deep root systems or triggered specific microbial carbon sequestration pathways. This is consistent with the research of Zang et al. [67], who found that leguminous crops can promote the formation of organic carbon components bound to minerals in deep soil through biological nitrogen fixation.

4.4. Research Limitations and Future Directions

This study did not cover the impact of crop rotation on soil carbon isotopes (such as δ¹³ C), making it difficult to quantify the contribution rate of carbon input from different crops. Furthermore, the composition of root secretions was not determined, and it was impossible to clarify the specific signaling pathways by which crop rotation regulates the microbial-enzyme system. In the future, stable isotope tracer technology can be combined to deeply analyze the cascade effect of root carbon input–microbial metabolism–soil carbon sequestration under the crop rotation mode, providing theoretical support for the construction of a low-carbon and efficient crop rotation system in Xinjiang cotton-growing areas.

5. Conclusions

The results of this study indicate that diversified crop rotation, particularly cotton–peanut rotation, significantly improve soil quality in long-term continuous cotton fields through enhanced carbon sequestration enzyme activity, increased bacterial alpha diversity, and elevated soil organic carbon content. Compared with long-term continuous cotton cropping, the cotton–peanut rotation significantly increased soil organic carbon and easily oxidizable organic carbon by 11.76% and 45.18%, respectively, enhanced RuBisCo activity by 80.96%, and markedly raised bacterial α-diversity in the 0–20 cm soil layer. Meanwhile, cotton–peanut rotation increased the soil quality index by 74.56% compared with cotton-continuous crop. Meanwhile, the optimal rotation pattern for improving the soil quality of long-term continuous cotton fields is cotton–peanut.