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Article

Cassava–Maize Rotation Improves Soil Quality and Microbial Gene Profiles Compared to Continuous Cassava Cropping

by
Yanmei Zhu
1,2,
Yundong Wei
3,* and
Xingming Qin
1,4,*
1
Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China
2
Guangxi Geographical Indication Crops Research Center of Big Data Mining and Experimental Engineering Technology, Nanning Normal University, Nanning 530001, China
3
Guangxi Subtropical Crops Research Institute, Nanning 530001, China
4
Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1999; https://doi.org/10.3390/agronomy15081999
Submission received: 13 July 2025 / Revised: 14 August 2025 / Accepted: 15 August 2025 / Published: 20 August 2025
(This article belongs to the Section Innovative Cropping Systems)
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Abstract

Due to limited land resources and traditional farming practices, continuous cassava cropping is common in China. This practice leads to soil degradation, including reduced fertility, imbalanced microbial communities, and lower crop yields. In this study, we investigated the impacts of continuous cassava cropping (CC) and cassava–maize rotation (RC) systems on soil physicochemical properties, microbial community composition, and functional gene abundance related to carbon and nitrogen cycling. The RC system consists of a five-year rotation cycle: cassava is planted in the first year, followed by two consecutive years of maize, and then, cassava is planted again in the last two years. The soil type is classified as Haplic Acrisols with a clay loam texture in this research. Soil samples from both cropping systems were analyzed for physicochemical properties and enzyme activities, and the results showed significant decreases in soil pH, available nitrogen, available phosphorus, and available potassium in CC. Using metagenomic sequencing, 1,280,928 and 1,224,958 unigenes were identified under RC and CC, respectively, with differences in microbial taxonomic and functional profiles. Bacteria accounted for 89.257% of the soil community in RC, whereas the proportion was 88.72% in CC. The proportions of eukaryota and viruses in RC were 0.031% and 0.006%, respectively; in contrast, their proportions were 0.04% and 0.02% in CC, respectively. Cassava–maize rotation promoted the metabolic activities of soil microbes, leading to a significant enhancement in functional genes related to nitrogen and carbon cycling, such as nasA, nasD, nrtC, coxA, porA, and frdA. This shows that microbial activity and nutrient cycling improved in the crop rotation system. Thus, these findings highlight the importance of crop rotation for maintaining soil health, enhancing microbial functions, and improving sustainable cassava production. This study provides valuable insights into the management of cassava agroecosystems and the mitigation of the adverse effects of continuous cropping.

1. Introduction

Cassava (Manihot esculenta Crantz) is characterized by a strong adaptability to poor soil and the ability to withstand drought stress [1]. Due to its edible starchy roots, it serves as a major food source for impoverished farmers in tropical and subtropical regions [2]. Additionally, cassava is widely used in the production of starch, ethanol, and sorbitol [3], playing a key role in bioenergy and food security. In recent years, demand has surged due to international geopolitical instability and climate change [4]. Cassava is one of the major economic crops in China. However, its cultivation area has been decreasing annually because of low planting efficiency. Cassava is primarily grown in the hilly regions of southern China, where effective irrigation systems are lacking [5]. Here, soil resources are mainly dominated by poor soil, and the continuous cassava planting pattern is popular [6]. Maize is one of the main crops in the hilly sloping land due to its adaptability to the local climate and soil conditions [7]. Therefore, incorporating maize into the cassava-based rotation system not only reflects local agricultural practices but also enhances the relevance of this study to regional agricultural production.
Continuous cropping obstacles (CCOs) refer to the phenomenon where the continuous planting of the same or closely related crops on the same land leads to an increase in crop diseases and pests, ultimately resulting in decreased crop yield and quality, despite the use of standard cultivation practices [8]. Studies have shown that CCOs can result from changes in physicochemical properties, an imbalance in the soil microbial community structure, and the accumulation of soil-borne pathogens [9,10]. The continuous cultivation of cassava has contributed to the gradual deterioration of soil physicochemical properties and microbial community structure imbalance, negatively impacting the development of roots and leading to reduced yields [11,12]. Limited research has focused on the causes and mitigation measures of CCOs in cassava. Some studies suggested that cassava–soybean intercropping could effectively alleviate CCOs by modulating soil metabolite accumulation and microbial community structure, thereby improving soil acidification, compaction, and nutrient depletion [13]. Fenlong tillage was found to create a more favorable soil environment for cassava growth than conventional tillage, promoting a more stable rhizosphere fungal community [6]. Soil bacterial abundance and diversity in the continuous cropping of cassava were higher than those in crop rotation [14]. Thus, soil microbial composition can be adjusted by adopting suitable cultivation management strategies, which is an effective approach to overcoming CCOs.
Microbes are the most active components of the soil ecosystem, as their diversity reflects not only their metabolic characteristics but also the complexity of their interactions with soil environmental conditions [15]. Conventional microbial investigations carried out using bacteria cultivation methods are limited in that many microbes cannot be cultured, therefore restricting the scope of microbiological research [16]. With the rapid development of sequencing technologies, metagenomic approaches have become increasingly popular in the study of soil microbial community structure and diversity [17]. Metagenomic analysis avoids the limits of traditional culture methods. By examining microbial gene sequences, it quickly identifies species, their abundance, and functions. This approach gives a more detailed and reliable view of microbial diversity [18]. Soil microbial diversity is tightly linked to carbon and nitrogen cycling [19]. Metagenomic techniques allow researchers to detect functional genes that participate in these elemental cycles [20]. By altering soil physiochemical characteristics, farming practices indirectly affect the microbial pathways involved in carbon and nitrogen turnover [21]. Among these, nitrogen cycling is vital for keeping nitrogen storage and availability in balance [22]. This process is regulated by genes related to nitrogen metabolism [23]. Key functional genes commonly studied include nifH (for nitrogen fixation); amoA (for ammonia oxidation); and several genes involved in denitrification, such as nirK, nirS, nirG, and nirZ [24,25]. Similarly, microorganisms are also key drivers of carbon cycling processes, including organic carbon degradation, carbon fixation, and methane metabolism [26]. The key functional genes that participate in the carbon cycle have been revealed through metagenomics, such as those involved in the citric acid cycle [27]. Notably, improved soil management can increase the nitrogen content, which then affects carbon-related processes. The interactions between nitrogen and carbon in soil systems are often interdependent and dynamic.
Although the role of soil microbial diversity in alleviating CCOs is increasingly recognized, the mechanisms by which different cassava cropping systems influence microbial carbon and nitrogen cycling remain unclear. To address this, we compared soils under continuous cassava cropping (CC) and cassava–maize rotation (RC). We hypothesized that (1) CC would significantly reduce soil physicochemical properties compared with RC; (2) CC would have higher microbial abundance but lower diversity, resulting in an imbalanced community structure; and (3) functional genes related to carbon and nitrogen cycling would be more abundant and diverse in RC, promoting more efficient nutrient cycling. We assessed soil physicochemical properties, enzyme activities, microbial community composition, and the abundance of functional genes involved in carbon and nitrogen cycling. The results help clarify how cassava cropping systems affect soil microbial processes and provide a basis for mitigating the negative impacts of continuous cassava cultivation.

2. Materials and Methods

2.1. Site Description and Sample Collection

The experimental site was located in Shuanglu Village, Ningwu Town, Wuming City, Guangxi Province, China (23°07′10″ N, 108°09′31″ E). The area has an altitude of about 200 m and a subtropical monsoon climate, with a mean annual temperature of 21.7 °C. The minimum and maximum mean monthly temperatures are −0.8 °C and 40.7 °C in January and July, respectively, with a total annual average precipitation of 1300 mm. The region experiences an annual average sunshine duration of 1660 h. The experimental plots were located in the same region, with sugarcane as the previous crop, and shared the same soil type and management histories prior to the implementation of different cropping systems. According to the World Reference Base for Soil Resources, the soil type is classified as Haplic Acrisols with a clay loam texture [28].
Two different cropping systems were adopted in two regions: one was a cassava–maize rotation system, following the sequence cassava–maize–maize–cassava–cassava (RC), and the other was continuous cassava cultivation for five years (CC). The RC system consisted of five consecutive growing seasons over five years: cassava in the first year, maize in the second and third years, and cassava in the fourth and fifth years. Cassava was generally planted in March and harvested in December–January of the following year, with each season lasting approximately 10–11 months. Maize was planted twice each year: in spring (sown in March and harvested in June) and in autumn (sown in August and harvested in December), with each season lasting about 3–4 months. This rotation scheme reflects local agronomic practices in the study region. Cassava was planted using conventional tillage, where the soil was first ploughed to a depth of 35–40 cm using a tractor, followed by harrowing to a depth of 20 cm. The cassava variety planted was Nanmei No. 1. The experimental design followed a randomized complete block design with three replicates for each cropping system. Each region was divided into three plantation plots, each measuring approximately 20 m × 33 m (≥660 m2). In the years when maize was planted, the soil was first ploughed to a depth of 20 cm. Following maize harvest, maize straw was retained on the soil surface to function as a mulch layer. Cassava and maize were fertilized according to conventional local practices, with application rates of 180 kg/hm2 N, 90 kg/hm2 P2O5, and 180 kg/hm2 K2O for cassava and 120 kg/hm2 N, 30 kg/hm2 P2O5, and 120 kg/hm2 K2O for maize.
The sampling date was chosen at the harvest period of cassava to avoid the potential influence of fertilization and other field management practices on the measured parameters. On 28 December 2022, rhizosphere soil samples of the cassava plants were randomly collected from each experimental plot in the two regions using a five-point sampling method during the fifth year of cultivation. First, the whole tuberous roots were carefully taken out after removing the topsoil, and then, sterile brushes and tweezers were used to collect the soil attached to the root surface. All the collected soil samples were stored in cool boxes and transported to the laboratory. Soil samples were divided into three portions: one portion was stored at −80 °C for the extraction of DNA; one portion was stored at 4 °C for the determination of urease activity and sucrase as soon as possible; and the remainder was air-dried at room temperature and passed through a 2 mm mesh for the measurement of soil properties, such as hydrogen ion concentration (pH), soil organic matter (SOM), total nitrogen(TN), available nitrogen (AN), total phosphorus (TP), available phosphorus (AP), and available potassium (AK).

2.2. DNA Extraction and Metagenomic Sequencing

The total DNA of the soil was extracted using a Magnetic Blood Genomic DNA Extraction kit (Tiangen, Beijing, China) following the manufacturer’s instructions. The integrity of the DNA was checked using 1% agarose gels, and the concentration was quantified using a Qubit dsDNA Assay Kit on a Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). Metagenome shotgun sequencing libraries were constructed after processing the pure DNA in the extraction solution using a NEBNext Ultra DNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA). Quantified libraries were pooled and sequenced on the Illumina HiSeq X-ten platform (Illumina, San Diego, CA, USA) using the PE150 strategy, according to the effective library concentration and data amount required.
The raw data from the Illumina sequencing platform were preprocessed using Readfq (V8, https://github.com/cjfields/readfq, accessed on 18 January 2023) to obtain clean data for subsequent analysis. Then, MEGAHIT software (v1.0.4-beta) was used for an assembly analysis of the clean data to obtain Scaftigs without N. MetaGeneMark (V3.05, http://topaz.gatech.edu/GeneMark/, accessed on 18 January 2023) was used to perform ORF prediction for the Scaftigs (≥500 bp) of each sample. For the ORF prediction results, CD-HIT software (V4.5.8) was used to eliminate redundancy and obtain the non-redundant initial gene catalogue. The clean data of each sample were aligned to the initial gene catalogue by using Bowtie2 (Bowtie2.2.4) to calculate the number of reads of the genes on each sample alignment. Genes with reads ≤ 2 in each sample were filtered out to finally determine the gene catalogue (unigenes) for subsequent analysis.
DIAMOND software (V0.9.9.110) was used to align the sequences of the unigenes with those of the bacteria, fungi, archaea, and viruses extracted from NCBI’s NR database (Version 2018-01-02). From the alignment results of each sequence, the one with an evalue ≤ min. evalue*10 was selected. As each sequence may have had multiple alignment results, the LCA algorithm was adopted to determine the species annotation information of the sequence. Based on the results of the LCA annotation and gene abundance table, the abundance of each sample at each taxonomic level (kingdom, phylum, class, order, family, genus, and species) was acquired. DIAMOND software (v0.9.9) was used to align the unigenes with those in the functional database, including the KEGG database (http://www.kegg.jp/kegg/, accessed on 28 January 2023), eggNOG database (http://eggnogdb.embl.de/#/app/home, accessed on 28 January 2023), and CAZy database (http://www.cazy.org/, accessed on 28 January 2023).

2.3. Determination of Soil Physicochemical Properties and Enzymatic Activities

The soil samples were cleaned by removing the roots, gravel, and other impurities after air-drying naturally, and then, they were ground and sieved as needed for the experiment. The soil physicochemical properties were determined following the procedures described by Bao [29]. The pH of the soil was determined using a pH meter, with a soil-to-water ratio of 1:2.5. The SOM content was measured using the K2Cr2O7 oxidation method combined with heating. The TN was determined using the Kjeldahl method. The TP content was measured using the molybdenum–antimony colorimetric method. The AN content was determined using the alkaline hydrolysis diffusion method. The AP content was determined using the NaHCO3 extraction method, followed by the molybdenum–antimony colorimetric method. The AK content was measured using the ammonium acetate–flame photometry method. Sucrase (SUC) and urease (URE) activities were measured using the 3,5-dinitrosalicylic acid colorimetric method and indophenol blue colorimetric method, respectively [30]. All measurements were performed in triplicate.

2.4. Statistical Analysis

The means and standard deviations of the raw data were calculated using Microsoft Excel 2019. The changes in soil chemical properties, soil enzyme activity, and gene abundance were analyzed for significant differences using a one-way analysis of variance (ANOVA) in SPSS 26.0 software (SPSS Inc., Chicago, IL, USA), followed by Duncan’s multiple comparison test (p < 0.05). All experimental data are presented as the mean ± standard deviation. A Pearson correlation analysis was performed to assess the relationships between environmental factors, soil enzyme activity, and functional gene abundance. A redundancy analysis (RDA) was conducted to explore the associations between environmental factors and microbial abundance at the phylum and genus levels using Canoco 5 software. The results were visualized using an online analysis platform (https://cnsknowall.com/#/Home/Contain/BottomContainAll, accessed on 29 March 2025).

3. Results

3.1. Soil Physicochemical Properties and Enzymatic Activities

Figure 1 shows the physicochemical properties and enzyme activities of the rhizosphere soil in the RC and CC systems. After five years of continuous cassava cropping, the soil pH (Figure 1a), SOM content (Figure 1b), and available nutrients (AN, AP, and AK) (Figure 1d,f,g) significantly decreased, indicating soil acidification and a reduction in the available nutrients due to continuous cropping. The TN content in CC was higher than that in RC, but the difference was not significant (Figure 1c). No significant differences were observed in enzyme activities between the two treatments (Figure 1h,i). The urease activity in RC was slightly higher than that in CC, whereas the sucrase activity in RC was slightly lower than that in CC.

3.2. Metagenomic Sequencing Information and the Composition of Soil Microbial Communities

Six libraries were sequenced on the Illumina Hiseq platform. Overall, 40,778.93 Mbp of raw reads was generated, and 40,692.60 Mbp of clean reads was subsequently obtained after filtering (Supplementary Table S1). In total, 1,280,928 and 1,224,958 unigenes were identified in RC and CC, respectively, and 1,169,047 of these were common in both groups (Figure 2a,b). The taxonomic annotation of the gene catalog identified a total of 134 bacterial phyla and 10,031 bacterial species, 7 fungal phyla and 112 fungal species, and 16 archaeal phyla and 491 archaeal species. The microbial community compositions were affected by the different farming systems. In RC, bacteria accounted for 89.257% of the soil community compared with 88.72% in CC. Eukaryota and viruses made up 0.031% and 0.006% in RC and 0.04% and 0.02% in CC, respectively. This indicates that cassava–maize rotation may promote bacterial growth and limit the accumulation of eukaryota and viruses (Figure 2c,d). The data show that Actinobacteria, Proteobacteria, and Acidobacteria were the dominant microbial phyla in both groups (Figure 2e). Compared to RC, CC decreased the relative abundance of Actinobacteria and Proteobacteria while increasing that of Acidobacteria. The dominant microbial genera in each group were Streptomyces, Bradyrhizobium, and Ktedonobacter (Figure 2f). Compared to RC, CC increased the relative abundance of Streptomyces while decreasing that of Bradyrhizobium and Ktedonobacter.

3.3. Annotation and Classification of Soil Microbial Functions

The functional annotation and classification of genes were conducted in the KEGG, eggNOG, and CAZy databases. Based on the results of the primary functional annotation in the KEGG database, the most enriched category was related to “metabolism”, followed by “genetic information” and “environmental information processing” (Figure 3a). Overall, continuous cropping decreased the gene abundance of the six primary functional metabolic pathways (Figure 3b). Figure 3c shows the top 10 most abundant primary function categories in the eggNOG database, of which the gene abundance was higher in RC than in CC, except for “Signal transduction mechanisms”, “Cell wall/membrane/envelope biogenesis”, and “Function unknown” (Figure 3d). In the CAZy database, the most enriched functional category was related to “Glycoside Hydrolases”, of which the gene abundance was higher in RC than in CC (Figure 3e,f).

3.4. Response of Functional Groups Involved in Nitrogen Cycling to Different Farming Systems

Based on the KEGG database annotation, the diagram of the nitrogen cycle was constructed (Figure 4). Key functional genes related to soil nitrogen cycling were selected for analysis (Figure 5), including nine nitrification (Figure 5a), five denitrification (Figure 5b), five assimilatory nitrate reduction (Figure 5c), seven dissimilatory nitrate reduction (Figure 5d), and fourteen nitrogen uptake genes (Figure 5e,f). The different cropping systems exerted regulatory effects on the expression of nitrogen cycling-related genes in the cassava field. Overall, the abundance of nitrogen cycling genes was higher in the crop rotation system than in the continuous cropping system. The abundance of the NasA, nasD, and natC genes in RC was significantly higher than that in CC (p < 0.01). In addition, several other nitrogen cycling-related genes, including pmoB-amoB, nrtP, narK, NRT, nasF, cynA, and nrtA, also exhibited a higher abundance in RC than in CC (p < 0.05). In contrast, the abundance of the nasE gene was lower in RC than in CC. Genes related to the anammox and nitrogen fixation pathways were not detected in this study.

3.5. Response of Functional Groups Involved in Carbon Cycling to Different Farming Systems

The microbial genes and their abundance in the carbon cycle were investigated in this study, and they are shown in Figure 6. In this study, the functional genes involved in the carbon cycle that were annotated were classified into five categories: aerobic carbon fixation (six genes), fermentation (two genes), aerobic respiration (two genes), anaerobic carbon fixation (two genes), and carbon monoxide oxidation (two genes). The expression of genes related to microbial involvement in carbon cycling in cassava soil was regulated by the different cropping systems. Overall, a higher abundance of carbon cycling-related microbial genes was observed in the crop rotation system than in the continuous cropping system. The abundance of the coxA gene was extremely significantly higher in RC than in CC (p < 0.01). Additionally, the abundances of the frdA and porA genes were also significantly affected by the different farming systems. Genes related to the methanogenesis and methane oxidation pathways were not detected in this study.

3.6. Correlation Between Physicochemical Properties, Soil Enzyme Activities, and Car Bon and Nitrogen Cycle-Related Genes

Figure 7a presents the results of the Pearson correlation analysis between the soil properties and nitrogen cycling-related genes. The results show that the narH and nirB genes, which are involved in dissimilatory nitrate reduction, exhibited a significant positive correlation with pH and AP (p < 0.01). In addition, AK showed a significant negative correlation with the nrfH and nirD genes (p < 0.01). The nasA and narB genes, which are involved in the dissimilatory nitrate reduction pathway, showed a significantly positive correlation with pH and SOM (p < 0.01). The nirS gene, which is typically linked to nitrification/denitrification processes, was positively associated with TN (p < 0.01). A significant positive relationship was also observed between the nxrB gene and soil pH (p < 0.01). For the genes involved in nitrogen uptake, nasD correlated positively with AP (p < 0.01), and the nrtP, narK, and NRT genes were all positively associated with AK (p < 0.01). Furthermore, the nrtB and cynB genes showed significant positive correlations with TP (p < 0.01), while the nasF, cynA, and nrtA genes were positively correlated with AP (p < 0.01).
Figure 7b presents the Pearson correlation analysis between the soil properties and genes related to carbon cycling. The results show that the coxA gene, which is involved in aerobic respiration, was significantly positively correlated with pH (p < 0.01). The frdA and accA genes, which are associated with anaerobic carbon fixation, were significantly positively correlated with AP and AK, respectively (p < 0.01). Additionally, the porA gene, which is associated with aerobic carbon fixation, showed a significant positive correlation with both pH and AP (p < 0.01).

3.7. Relationship Between Soil Physicochemical Properties and Microbial Community

A redundancy analysis (RDA) was performed using the top 10 species at both the phylum and genus levels and soil physicochemical properties (Figure 7c,d). The results show that axes 1 and 2 together explained 99.28% of the variation in microbial communities at the phylum level and 98.79% at the genus level, indicating a significant correlation between soil physicochemical factors and microbes (Figure 7). At the phylum level (Figure 7c), the environmental factors with the greatest influence on the microbial community were TN and SOM, explaining 51.6% and 36.0% of the variation, respectively. At the genus level (Figure 7d), the key environmental factors influencing the microbial community were TP and TN, explaining 71.6% and 14.9% of the variation, respectively. The TN content was negatively correlated with the other soil physicochemical indicators.

4. Discussion

4.1. Impact of Continuous Cropping on Soil Physicochemical Properties

Microbial growth in soil is influenced by soil physicochemical characteristics and enzymatic activities, which consequently modulate the microbial community structure and affect plant growth and development [31,32]. Rhizosphere microorganisms are a diverse group of microscopic organisms that reside in the rhizosphere, which is a narrow soil zone directly impacted by root exudates and the associated soil microorganisms [33]. The population structure and function of rhizosphere microorganisms can be affected by soil physicochemical properties, including texture, pH, organic matter, and nutrient content [34,35].
Soil pH determines the growth environment for plant roots. The availability of the majority of plant nutrients, if not all, is controlled by soil pH [36]. In this study, continuous cassava cropping resulted in a significantly lower soil pH but a significantly higher total nitrogen content than crop rotation (Figure 1a). This suggests that continuous cassava cropping led to soil acidification, a finding consistent with that of previous studies [11]. It is believed that soil acidification may be associated with the annual application of large amounts of nitrogen fertilizer in cassava cultivation, while the nitrogen fertilizer use efficiency remains low [37]. In addition, a decrease in pH directly affects microbial diversity and the soil ecosystem, resulting in a disrupted soil microecosystem and the proliferation of soil-borne diseases in arable soils [38]. SOM is regarded as a storage reservoir for soil nutrients and is known to significantly affect nearly all soil chemical, physical, and biological properties [39]. Therefore, SOM is generally considered an important criterion for the assessment of sustainable agricultural management [40]. In our study, the SOM content under continuous cassava cropping was significantly lower than that under crop rotation (Figure 1b). This difference was primarily attributed to the incorporation of maize straw in the crop rotation, which increased the soil organic matter content, whereas the fertilizers applied in cassava cultivation were primarily mineral fertilizers. Long-term continuous cassava cultivation led to the extensive depletion of soil organic matter, thereby exacerbating soil degradation. Therefore, attention should be paid to the supplementation of organic fertilizers in cassava farming management to maintain soil sustainability and health. Continuous cassava cropping significantly reduced the content of soil available nutrients (AN, AP, and AK) (Figure 1d,f,g), which might be the primary reason for the decline in yield and quality after several years of continuous cassava culture. According to Cui et al., peanut–cotton rotation effectively alleviated soil nutrient depletion and improved land utilization [41]. Therefore, enhancing crop diversification in cassava fields will benefit the increase in available soil nutrients, which, in turn, will promote cassava plant growth and development. This approach will be a key measure to mitigate the obstacles posed by continuous cassava cropping in the future.

4.2. Metagenomics Reveals Soil Microbial Community Composition in Different Farming Systems

Soil microbes play an essential role in nutrient cycling by breaking down organic matter into forms that plants can absorb, thus supporting overall soil health [42]. The composition of soil microbial communities is influenced by various factors, including farming practices, soil type, organic inputs, and the types of plants grown [43,44,45]. Repeated planting of the same crop, known as continuous cropping, has been shown to disturb the structure and diversity of soil microbial communities. For instance, the continuous cultivation of Panax notoginseng led to increased levels of Acidobacteria, Gemmatimonadetes, and Chloroflexi, while fallow treatment reduced the levels of Actinobacteria [46]. Similarly, Glycyrrhiza uralensis under continuous cropping exhibited a decrease in bacterial diversity and an increase in fungal richness compared to that under crop rotation [47]. In the case of Codonopsis pilosula, continuous cultivation significantly shifted the microbial structure by lowering bacterial diversity and boosting Acidobacteria abundance [48]. The long-term continuous cropping of both grain and medicinal crops was also linked to a decrease in beneficial microbes, such as Actinomycetes, Acidobacteria, and Green Campylobacter, while harmful fungi, such as Alternaria and Didymellaceae, became more prevalent [49]. Additionally, in continuous maize cropping for five years, the soil bacterial community was strongly influenced by organic matter and organic carbon, with Actinobacteria dominating the maize field [34]. In our study, we observed similar trends in cassava systems. Continuous cassava planting reduced soil bacteria abundance relative to cassava rotation, while eukaryotes and viruses increased (Figure 2a,d). This indicates that continuous cassava cropping may suppress bacterial community diversity and encourage the accumulation of viruses, thereby negatively affecting cassava growth. These findings are in line with those of prior studies [11]. Across both cropping systems, Actinobacteria, Proteobacteria, and Acidobacteria were identified as dominant microbial phyla (Figure 2e). However, under continuous cassava cropping, the relative abundance of Actinobacteria and Proteobacteria decreased, whereas that of Acidobacteria increased. At the genus level, Streptomyces, Bradyrhizobium, and Ktedonobacter were the most prominent (Figure 2f). Under continuous cassava cropping, the relative abundance of soil Streptomyces increased, whereas that of Bradyrhizobium and Ktedonobacter decreased. To better interpret these shifts, microbial life strategies could offer valuable insights [50]. An RDA demonstrated that the microbial community composition was influenced by key soil physicochemical properties. At the phylum level, SOM and TN were the main variables influencing bacterial communities (Figure 7c); at the genus level, TP was the primary factor affecting bacterial communities (Figure 7d). Overall, these results highlight the importance of nutrient availability and soil properties in shaping bacterial communities [51].

4.3. Functional Annotation and Taxonomic Classification of Soil Microbial Communities in Different Cropping Systems

To explore the roles of various proteins and metabolites within soil microbial communities in different cropping systems, functional annotation was conducted using the COG, CAZy, and KEGG databases [52]. According to the primary functional annotation of the KEGG database, “metabolism” was the most prominent functional category (Figure 3a). However, continuous cropping resulted in a decline in gene abundance across six key metabolic pathways (Figure 3b). Based on these findings, we propose that continuous cassava cultivation might promote virus accumulation, which could subsequently suppress the metabolic functions of soil microorganisms. Using the eggNOG database, it was found that cassava rotation increased the abundance of genes, which were annotated to the function class of “Replication, recombination and repair” (Figure 3d). DNA recombination played a critical role in DNA replication and cellular survival, as reported in previous studies [53]. In the CAZy database, the most enriched functional class was related to “Glycoside Hydrolases”, of which the gene abundance was relatively higher in soil cassava rotation cropping than in cassava continuous cropping (Figure 3f). Glycoside hydrolases are essential enzymes involved in microbial metabolic processes, facilitating the degradation of complex carbohydrates [54]. Cassava–maize rotation enhanced microbial glycoside hydrolase activity, thereby promoting the degradation of carbohydrates to supply essential nutrients and energy for cassava growth.

4.4. Effects of Farming Systems on Microbial Nitrogen and Carbon Cycling Genes

The most critical soil element cycling processes are driven by the composition and functional structure of the microbial community [55]. Under cassava rotation, the abundance of functional genes related to carbon and nitrogen cycles was found to increase. It has been shown that the primary processes of energy flow and material cycling within soil ecosystems are reflected in the abundance of these functional genes in soil microbes [56]. In line with our findings, cassava–maize rotation supported a richer microbial community structure and a greater gene abundance, which facilitated more frequent carbon and nitrogen cycling within the soil and enhanced interactions with the surrounding environment. Key genes involved in bacterial nitrogen uptake, such as nasA, nasD, and nrtC, were notably more abundant under cassava rotation. Specifically, nasA is responsible for nitrate transport [57], while nasD and nrtC play critical roles in the efficient uptake and utilization of nitrate and nitrite through the ABC transporter system [58]. These genes work together to enable bacteria to effectively utilize environmental nitrate for growth and metabolic processes [59]. In our study, the abundance of nasA, nasD, and nrtC was significantly higher in cassava–maize rotation than in continuous cassava cropping (p < 0.01) (Figure 5), suggesting that crop rotation stimulated the expression of these key genes, thereby enhancing bacterial nitrate utilization and improving nitrogen cycling efficiency. Additionally, the abundance of the aerobic respiration gene coxA was significantly greater in RC than in CC (p < 0.01) (Figure 6b). Similarly, the levels of porA, which is involved in aerobic carbon fixation, and frdA, which is involved in anaerobic carbon fixation, were also elevated in RC (p < 0.05) (Figure 6b). These results suggest that crop rotation enhanced the metabolic functions of the soil microorganisms involved in carbon cycling in cassava fields. Genes associated with anammox, nitrogen fixation, methanogenesis, and methane oxidation pathways were not detected in this study, possibly due to genuinely low expression levels or limitations in detection methods and sensitivity.

5. Conclusions

The objective of this study was to compare the differences in the soil physicochemical and microbial compositions and functional genes between different types of cropping systems, i.e., cassava–maize rotation and continuous cassava cropping for 5 years. Our results show the following: (1) The continuous cultivation of cassava resulted in a significant decrease in the soil pH, SOM, and soil available nutrients, primarily because of the extensive use of nitrogen fertilizers with low efficiency and the lack of organic inputs; (2) using metagenomic sequencing, 1,280,928 and 1,224,958 unigenes were identified under RC and CC, respectively, with differences in microbial taxonomic and functional profiles; (3) continuous cassava cropping reduced bacterial abundance and favored the accumulation of eukaryotes and viruses, thereby negatively influencing soil health and plant growth; (4) functional gene analyses revealed that cassava–maize rotation increased the expression of genes related to nitrogen and carbon cycling, including nasA, nasD, nrtC, coxA, porA, and frdA, enhancing microbial metabolic activities and nutrient turnover. These results show that crop rotation plays a pivotal role in maintaining soil fertility, promoting beneficial microbial functions, and ensuring sustainable cassava production. Overall, adopting cassava–maize cropping systems offers an effective strategy to restore soil health, improve nutrient cycling, and alleviate the negative effects of continuous cropping. These findings provide crucial theoretical support and practical guidance for the sustainable management of cassava agroecosystems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15081999/s1, Table S1: Overview of the soil metagenomic sequencing.

Author Contributions

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

Funding

This work was supported by the Research Capacity Enhancement Program for Mid-Career and Young Faculty in Guangxi Universities (2025KY0445), the Guangxi Natural Science Foundation (2023GXNSFAA026440), and the Opening Foundation of Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University (NNNU-KLOP-X2006).

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The physicochemical properties and enzyme activities of the rhizosphere soil in the CC and RC systems. RC: cassava–maize rotation; CC: continuous cassava cropping for five years; pH: hydrogen ion concentration (a); SOM: soil organic matter (b); TN: total nitrogen content (c); AN: available nitrogen (d); TP: total phosphorus (e); AP: available phosphorus (f); AK: available potassium (g); URE: urease activity (h); SUC: sucrase activity (i). Different lowercase letters represent significant differences (p < 0.05). Error bars are the standard deviation.
Figure 1. The physicochemical properties and enzyme activities of the rhizosphere soil in the CC and RC systems. RC: cassava–maize rotation; CC: continuous cassava cropping for five years; pH: hydrogen ion concentration (a); SOM: soil organic matter (b); TN: total nitrogen content (c); AN: available nitrogen (d); TP: total phosphorus (e); AP: available phosphorus (f); AK: available potassium (g); URE: urease activity (h); SUC: sucrase activity (i). Different lowercase letters represent significant differences (p < 0.05). Error bars are the standard deviation.
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Figure 2. The metagenomic sequencing information and the soil microbial community compositions. The number of unigenes in both groups (a); the number of unigenes common to both groups (b); the proportions of soil microorganism species classified at the kingdom level under RC (c); the proportions of soil microorganism species classified at the kingdom level under CC (d); the relative abundances of soil microbial communities in the different groups at the phylum level (e); the relative abundances of soil microbial communities in the different groups at the genus level (f). RC: cassava–maize rotation; CC: continuous cassava cropping for five years.
Figure 2. The metagenomic sequencing information and the soil microbial community compositions. The number of unigenes in both groups (a); the number of unigenes common to both groups (b); the proportions of soil microorganism species classified at the kingdom level under RC (c); the proportions of soil microorganism species classified at the kingdom level under CC (d); the relative abundances of soil microbial communities in the different groups at the phylum level (e); the relative abundances of soil microbial communities in the different groups at the genus level (f). RC: cassava–maize rotation; CC: continuous cassava cropping for five years.
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Figure 3. The annotation and classification of soil microbial functions. The numbers of unigenes were annotated in the KEGG database for the primary functional annotation (a); the abundances of unigenes were annotated in the KEGG database for the primary functional annotation (b); the numbers of unigenes were annotated in the eggNOG database for the top 10 most abundant primary function categories (c); the abundances of unigenes were annotated in the eggNOG database for the top 10 most abundant primary function classes (d); the numbers of unigenes were annotated in the CAZy database for different functional classes (e); the abundances of unigenes were annotated in the CAZy database for different functional classes (f). RC: cassava–maize rotation; CC: continuous cassava cropping for five years.
Figure 3. The annotation and classification of soil microbial functions. The numbers of unigenes were annotated in the KEGG database for the primary functional annotation (a); the abundances of unigenes were annotated in the KEGG database for the primary functional annotation (b); the numbers of unigenes were annotated in the eggNOG database for the top 10 most abundant primary function categories (c); the abundances of unigenes were annotated in the eggNOG database for the top 10 most abundant primary function classes (d); the numbers of unigenes were annotated in the CAZy database for different functional classes (e); the abundances of unigenes were annotated in the CAZy database for different functional classes (f). RC: cassava–maize rotation; CC: continuous cassava cropping for five years.
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Figure 4. The functional genes related to soil nitrogen cycling. The contents inside the oval represent the pathways of the nitrogen cycle; genes are indicated in italicized font within rectangular boxes; arrows denote the direction of the pathways; the names of the pathways, related genes, and arrows are all shown in the same color. N2: nitrogen; NH4+: ammonium ion; NH2OH: hydroxylamine; NO2−: nitrite ion; NO3−: nitrate ion; N2H4: hydrazine; NO: nitric oxide; N2O: nitrous oxide.
Figure 4. The functional genes related to soil nitrogen cycling. The contents inside the oval represent the pathways of the nitrogen cycle; genes are indicated in italicized font within rectangular boxes; arrows denote the direction of the pathways; the names of the pathways, related genes, and arrows are all shown in the same color. N2: nitrogen; NH4+: ammonium ion; NH2OH: hydroxylamine; NO2−: nitrite ion; NO3−: nitrate ion; N2H4: hydrazine; NO: nitric oxide; N2O: nitrous oxide.
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Figure 5. The absolute abundance of unigenes in different pathways related to nitrogen cycling. The absolute abundance of unigenes in the pathways of nitrification (a), denitrification (b), assimilatory nitrate reduction (c), dissimilatory nitrate reduction (d), nitrogen uptake genes (e,f). ** represents extremely significant differences at p < 0.01, and * represents extremely significant differences at p < 0.05.
Figure 5. The absolute abundance of unigenes in different pathways related to nitrogen cycling. The absolute abundance of unigenes in the pathways of nitrification (a), denitrification (b), assimilatory nitrate reduction (c), dissimilatory nitrate reduction (d), nitrogen uptake genes (e,f). ** represents extremely significant differences at p < 0.01, and * represents extremely significant differences at p < 0.05.
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Figure 6. The functional genes related to soil carbon cycling (a) and the absolute abundance of unigenes in different pathways related to carbon cycling (b). The contents inside the oval represent the pathways of the carbon cycle; genes are indicated in italicized font within rectangular boxes; arrows denote the direction of the pathways; the names of the pathways, related genes, and arrows are all shown in the same color. CO2: carbon dioxide; CO: carbon monoxide; CH4: methane. ** represents extremely significant differences at p < 0.01, and * represents extremely significant differences at p < 0.05.
Figure 6. The functional genes related to soil carbon cycling (a) and the absolute abundance of unigenes in different pathways related to carbon cycling (b). The contents inside the oval represent the pathways of the carbon cycle; genes are indicated in italicized font within rectangular boxes; arrows denote the direction of the pathways; the names of the pathways, related genes, and arrows are all shown in the same color. CO2: carbon dioxide; CO: carbon monoxide; CH4: methane. ** represents extremely significant differences at p < 0.01, and * represents extremely significant differences at p < 0.05.
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Figure 7. The Pearson correlation analysis between soil properties and nitrogen cycling-related genes (a); the Pearson correlation analysis between soil properties and carbon cycling-related genes (b); the RDA analysis between the top 10 species of soil microbes at both the phylum and genus levels and soil physicochemical properties (c,d). ** represents extremely significant differences at p < 0.01, and * represents extremely significant differences at p < 0.05.
Figure 7. The Pearson correlation analysis between soil properties and nitrogen cycling-related genes (a); the Pearson correlation analysis between soil properties and carbon cycling-related genes (b); the RDA analysis between the top 10 species of soil microbes at both the phylum and genus levels and soil physicochemical properties (c,d). ** represents extremely significant differences at p < 0.01, and * represents extremely significant differences at p < 0.05.
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Zhu, Y.; Wei, Y.; Qin, X. Cassava–Maize Rotation Improves Soil Quality and Microbial Gene Profiles Compared to Continuous Cassava Cropping. Agronomy 2025, 15, 1999. https://doi.org/10.3390/agronomy15081999

AMA Style

Zhu Y, Wei Y, Qin X. Cassava–Maize Rotation Improves Soil Quality and Microbial Gene Profiles Compared to Continuous Cassava Cropping. Agronomy. 2025; 15(8):1999. https://doi.org/10.3390/agronomy15081999

Chicago/Turabian Style

Zhu, Yanmei, Yundong Wei, and Xingming Qin. 2025. "Cassava–Maize Rotation Improves Soil Quality and Microbial Gene Profiles Compared to Continuous Cassava Cropping" Agronomy 15, no. 8: 1999. https://doi.org/10.3390/agronomy15081999

APA Style

Zhu, Y., Wei, Y., & Qin, X. (2025). Cassava–Maize Rotation Improves Soil Quality and Microbial Gene Profiles Compared to Continuous Cassava Cropping. Agronomy, 15(8), 1999. https://doi.org/10.3390/agronomy15081999

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