Interplanting of Corn (Zea mays L.) Shifts Nitrogen Utilization by Promoting Rhizosphere Microbial Nitrogen Nitrification

Abstract

Interplanting is an efficient method of improving nutrient utilization. However, the impact of intraspecific interplanting on rhizosphere microbial nitrogen cycling needs to be studied further. In this study, two corn cultivars were selected as the materials: Zhengdan958 (ZD958, high nitrogen use efficiency) and Denghai3622 (DH3622, low nitrogen use efficiency). Three planting patterns (interplanting, ZD958 monocropping, and DH3622 monocropping) were set up to study the effects of interplanting on crop growth and rhizosphere microbial nitrogen cycle function under two nitrogen levels: low nitrogen (140 kg N ha⁻¹) and normal nitrogen (280 kg N ha⁻¹). The results showed that the grain yield and nitrogen content in interplanting were significantly increased due to an enhanced leaf area index and root dry weight. The nitrogen accumulation and nitrogen use efficiency were enhanced by 8.14% and 19.38% in interplanting, which resulted in reductions in NH₄⁺ and NO₃⁻ content in the rhizosphere. Interplanting enhanced rhizosphere nitrogen cycling processes; nitrification, denitrification, and nitrate reduction were increased. This study demonstrated that interplanting promotes corn nitrogen acquisition from the soil and indirectly regulates rhizosphere microbial function. These findings imply that the intraspecific interplanting of crops with appropriate functional traits is a promising approach to establishing diversified, productive, and efficient resource utilization ecosystems.

1. Introduction

Corn is a globally cultivated crop and the largest grain crop in China, with a yield of 277 million tons [1]. A high yield of corn requires a large amount of nitrogen, so a nitrogen supply is essential for its growth and development [2]. Farmers have often used excessive N expecting a high yield [3], but it has been counterproductive. Over-fertilization results in a low N use efficiency and low economic benefits. It also exacerbates nitrogen leaching and volatilization, and causes a range of environmental problems, such as soil acidification and the eutrophication of water bodies [4,5]. Meanwhile, corresponding shifts in soil microbial properties have been widely reported, including the depletion of soil microbial biomass, alterations in species composition, and decreases in diversity [6,7]. Moreover, N fertilizer overuse greatly influences the soil N cycling process by regulating the abundance of N cycle-related microbes and genes [8]. A worldwide meta-analysis showed that soil microbial biomass was reduced by an average of 15% with nitrogen application, and the abundance of bacteria and fungi further declined with longer fertilization periods and higher N application rates [9]. Research found that microbial activity would decline when the nitrogen application exceeded 180 kg ha⁻¹ [10]. Therefore, it is necessary to improve the efficiency of nitrogen fertilizer use to ensure environmental health and food security.

Intercropping is the cultivation of two or more crops simultaneously on the same field [11]. Previous research showed that intercropping could improve nutrient and water use efficiency, maintain soil fertility, and reduce fertilizer demand. This allowed the production of crops to reach its full potential and achieved a high and stable yield [12]. Meanwhile, intercropping could increase soil microbial diversity through root exudates and nutrient absorption, which dilute the quantity of harmful bacteria [13,14]. The impact of intercropping on soil microorganisms has been reported in recent years. In a sugarcane and soybean intercropping system, intercropping changed the structure of the soil microbial community, which increased microbial diversity [15]. The same results were also found in intercropping between melon and cowpea, wheat and soybean, and potato and legume plants [16,17,18]. Variations in the microbial community inevitably give rise to variations in microbial function. In a legume intercropping system, soil nitrogen fixation function was promoted by increasing the abundance of nitrogen-fixing bacteria, such as Bradyrhizobium and Skermanella [19,20]. Intercropping could also reduce the relative abundance of denitrifying bacteria (Proteobacteria) by decreasing soil nitrate residues, achieving a suppression of N₂O emissions [15,21].

Interplanting, i.e., planting multiple varieties of one crop in the same field, is one special type of intercropping [22]. It can enrich the intraspecific and genetic diversity of crops in a field, which is particularly suitable for intensive production and could reduce biotic stressors such as disease, weeds, and insects [23]. The interplanting of corn with different traits enhanced disease resistance to leaf spot and leaf rust through intraspecific complementation [24,25]. The occurrence of rice blast also reduced in rice interplanting populations [26]. Meanwhile, interplanting could mitigate the damage caused by abiotic stressors (high temperatures, drought) [27]. For example, the interplanting of corn increased lodging resistance by 82.4% [28]. The duration of anthesis and the pollination of the population were also prolonged in an interplanting system, and the population fertilization rate was significantly improved at the silking stage under continuous rain and high temperature [29,30]. Interplanting also made full use of spatiotemporal niche complementarity and intraspecific diversity, staggering the maximum demand period of plants for light [23]. In an interplanting system of corn of different phenotypes (regarding, for example, plant height and leaf angle), the population’s light utilization was increased due to enhanced ventilation and light transmission and improvements in the intercepted photosynthetically active radiation [31]. However, little is known about the interactions between the root and soil microbial communities in interplanting systems.

Previous studies have focused on the microbial community structure in intercropping systems of different crops [15,16,17,18,19,20]. Furthermore, most studies focused on interplanting with the same crop species have been limited to disease resistance and cross-fertilization. In this study, we paid special attention to the effect of interplanting with different traits on plant nitrogen accumulation, the rhizosphere nitrogen cycle pathway, and their relationships. Field experiments were conducted to test the following hypotheses: (1) the interplanting of corn promotes plant growth and grain yield and enhances the leaf area index; (2) interplanting affects plant nitrogen utilization; and (3) interplanting increases rhizosphere microbial nitrogen cycle function by promoting crop growth.

2. Materials and Methods

2.1. Experiment Design and Crop Management

The field experiment was conducted during the summers of 2021 and 2022 at Henan Agricultural University experimental station (33°19′48″ N, 114°01′01″ E) in Xiping county, Henan Province, China. The mean annual temperature and rainfall are 15.8 °C and 910 mm. The soil is lime concretion black soil, with an organic matter content of 13.65 g kg⁻¹, a total N content of 1.25 g kg⁻¹, an alkaline hydrolytic nitrogen (N) content of 25.57 mg kg⁻¹, an Olsen phosphorus (P) content of 18.13 mg kg⁻¹ and a NH₄OAc extractable potassium (K) content of 124.91 mg kg⁻¹ at 0–20 cm soil layer.

Based on previous experimental results, two corn cultivars were selected as the materials: Zhengdan 958 (ZD 958, high nitrogen efficiency) and Denghai 3622 (DH 3622, low nitrogen efficiency) [32]. Two-year field experiments were arranged as a two-factor randomized block design with three replications. The experiment was composed of three planting patterns (ZD 958 and DH 3622 interplanting, ZD 958 monocropping, and DH 3622 monocropping) and two levels of N application (140 and 280 kg N ha⁻¹), denoted as LN (low nitrogen) and NN (normal nitrogen). This resulted in 6 treatments with 18 plots in total, covering a monocropping plot area of 480 m² and an interplanting plot area of 720 m². The seeds were sown on 15 June 2021 and 10 June 2022, respectively. In the field experiment, the monocrop corns were planted in 8-row plots and the interplanting corns were planted in 12-row plots (2 cultivars planted alternately in 2 rows) with 0.60 m inter-row spacing and 0.25 m in-row spacing to obtain a plant population of 67,500 plants ha⁻¹ (Figure 1).

The soil tillage adopted no-tillage in the maize season. Calcium superphosphate, potassium chloride, and urea were incorporated as basal fertilization (broadcast fertilization) at 10 cm depth during sowing at rates of 90 kg P ha⁻¹, 90 kg K ha⁻¹, and 70 and 140 kg N ha⁻¹. Other urea (70 and 140 kg N ha⁻¹) was implemented at the sixth leaf stage (V6) of corn in the LN and NN treatments. Crop irrigation and pest control followed the local best management practices.

2.2. Plant Measurements

According to the definition of leaf area index, three plants were selected from each plot to measure the maximum leaf length (L) and leaf width (W) at the sixth leaf stage (V6), the twelfth leaf stage (V12), the silking stage (R1), the blister stage (R2), and the mature stage (R6). Leaf area per plant was calculated by multiplying a weighting factor equal to 0.75 [33].

Leaf area index (LAI) (1) was calculated as follows:

$$\mathrm{LAI}=\mathrm{LA} \times \mathrm{LAU}/\mathrm{U}$$

(1)

where LA is leaf area per plant, LAU is number per unit land area, and U is unit land area.

Root weight was evaluated by collecting soil samples at the R1 stage in the field. Three adjacent plants were harvested, and their roots were dug vertically from the soil using a sharp-edged iron core from 0 to 60 cm. These was divided into six layers with a thickness of 10 cm for each layer, and each layer was separated into 6 small pieces with size of 20 cm length × 10 cm width × 10 cm thickness. The soil was then washed with flowing water. Subsequently, the roots were dried at 70 °C for 72 h and then weighted.

To determine the dry matter weight and nitrogen concentration, four plants from each plot at the R1 and R6 stages were randomly selected, cut at soil level, dried at 70 °C for 72 h, and then weighed. The N concentration of the samples was determined using a flow injection auto-analyzer (AA3, SEAL Analytical, Norderstedt, Germany) after digestion of samples with H₂SO₄-H₂O₂, and the indicators below were calculated [8].

Dry matter accumulation post anthesis (DMA, g plant⁻¹) (2), nitrogen accumulation post anthesis (NA, mg plant⁻¹) (3), and nitrogen use efficiency (NUE, %) (4) were calculated as follows:

$$\mathrm{DMA}={\mathrm{DMW}}_{\mathrm{mat}} - {\mathrm{DMW}}_{\mathrm{sil}}$$

(2)

$$\mathrm{NA}={\mathrm{NC}}_{\mathrm{mat}} - {\mathrm{NC}}_{\mathrm{sil}}$$

(3)

$$\mathrm{NUE}=({\mathrm{NC}}_{\mathrm{mat}} - {\mathrm{NCB}}_{\mathrm{mat}})/\mathrm{Na}$$

(4)

where DMW_mat is dry matter weight at maturity, DMW_sil is dry matter weight at silking, NC_mat is nitrogen content at maturity, NC_sil is nitrogen content at silking, NCB_mat is nitrogen content in blank treatment at maturity, and Na is nitrogen application (140 and 280 kg N ha⁻¹).

Grain yield was determined at the R6 stage, where 30 ears were harvested to determine corn yield from three rows in the center of each plot.

2.3. Soil Chemical Analysis

Rhizosphere soil was collected for the analysis of soil chemical properties and macrogenomes at the R1 stage in 2022. Three plants from each plot were grouped into one replicate, and each treatment was replicated 3 times from 3 plots. After root residues and grits were removed, the mixed soil sample was sieved (<2 mm) and divided into three parts. For the first part of the samples, around 10 g of fresh soil was sealed in 15 mL tubes and stored in liquid nitrogen for determination of the relative abundance of soil microorganisms. The second part of the samples was used to measure net potential nitrogen mineralization and nitrification [34]. The third part was used for soil chemical analyses. Soil samples were preprocessed using a combined H₂SO₄-H₂O₂ digestion method. Olsen phosphorus and extractable potassium were extracted using 0.5 M NaHCO₃ and 1 M NH₄Ac solution. NH₄⁺ and NO₃⁻ were extracted using 2 M KCl solution. Total nitrogen, NH₄⁺ and NO₃⁻ were detected with a continuous flow analyzer (AA3, SEAL Analytical, Norderstedt, Germany). Total phosphorus and Olsen phosphorus were measured with the molybdenum–antimony colorimetry method using a spectrophotometer (Thermo-Fisher Evolution 350, Waltham, IL, USA). Total potassium and extractable potassium were determined using a flame photometer (Cole-parmer FF-200D-I, Vernon Hills, UK).

2.4. Macrogenomes of Rhizosphere Soil Microorganisms

Total genomic DNA was extracted from the rhizosphere soil samples using the E.Z.N.A.^® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions. The concentration and purity of the extracted DNA were determined with TBS-380 (Turner Biosystems, Sunnyvale, CA, USA) and NanoDrop2000 (Thermo Scientific, Wilmington, DE, USA), respectively. DNA extract quality was tested on 1% agarose gel. The DNA extract was fragmented to an average size of about 400 bp using Covaris M220 (Gene Company Limited, Shanghai, China) for paired-end library construction. A paired-end library was constructed using NEXTflex^TM Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full complement of sequencing primer hybridization sites were ligated to the blunt end of the fragments. Paired-end sequencing was performed on Illumina NovaSeq/HiSeq Xten (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq Reagent Kits/HiSeq X Reagent Kits according to the manufacturer’s instructions (www.illumina.com, accessed on 3 September 2022). The raw reads from metagenome sequencing were used to generate clean reads by removing adaptor sequences, trimming and removing low-quality reads (reads with N bases, a minimum length threshold of 50 bp, and a minimum quality threshold of 20) using fastp on the free online Majorbio Cloud Platform (cloud.majorbio.com, accessed on 29 September 2022). These high-quality reads were then assembled to contigs using MEGAHIT (parameters: kmer min = 47; kmer max = 97; step = 10) (https://github.com/voutcn/megahit, accessed on 5 October 2022, version 1.1.2), which makes use of succinct de Bruijn graphs. Contigs with a length of, or over, 300 bp were selected as the final assembling result. Open reading frames (ORFs) in contigs were identified using MetaGene. The predicted ORFs with lengths of, or over, 100 bp were retrieved and translated into amino acid sequences using the NCBI translation table. A non-redundant gene catalog was constructed using CD-HIT with 90% sequence identity and 90% coverage. After quality control, the reads were mapped to the non-redundant gene catalog with 95% identity using SOAPaligner, and gene abundance in each sample was evaluated. Representative sequences of the non-redundant gene catalog were annotated based on the NCBI NR database using blastp as implemented in DIAMOND v0.9.19 with an e-value cutoff of 1 × 10⁻⁵ using Diamond for taxonomic annotations. The KEGG annotation was conducted using Diamond against the Kyoto Encyclopedia of Genes and Genomes database with an e-value cutoff of 1 × 10⁻⁵.

2.5. Statistical Analyses

The Wilcoxon rank-sum test was used to analyze the effect of planting patterns on the relative abundance of pathway level 2 in KEGG at the 5% level. The Bray–Curtis distance based on the module relative abundance of nitrogen cycle in KEGG was calculated to represent the variation in functional compositional among these samples. An analysis of similarities (ANOSIM) using Bray–Curtis was conducted to test the similarities and differences in the microbial functional gene composition prior to the β-diversity analysis. Principal coordinate analysis (PCOA) was used to visualize the relative differences between the samples [35]. Two-way ANOVA was used to analyze the effects of planting patterns and nitrogen levels on the modulation of the nitrogen cycle in KEGG, and the effects of the difference in planting pattern at the same nitrogen level were analyzed using the Tukey test at p < 0.05. Functional annotation was carried out by comparing the sequences of non-redundant gene catalog with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database on BLASTP (e-value < 1 × 10⁻⁵).

Other data were analyzed statistically using SPSS 20.0 [36]. The effects of nitrogen levels, planting patterns, cultivars, and their interactions were analyzed using three-way ANOVA. The statistical analysis of planting patterns and cultivars was carried out using two-way ANOVA. The statistical analysis of planting patterns at the same nitrogen level was carried out using the Tukey test at p < 0.05. All figures were plotted using Origin 2023 [37]. The structural equation model was analyzed using IBM SPSS Amos 28 [36].

3. Results

3.1. Nitrogen Utilization of Corn

Interplanting had a positive effect on the corn nitrogen content (NC) (Figure 2a), nitrogen use efficiency (NUE) (Figure 2b), and nitrogen accumulation post anthesis (NA) (Figure S1). Compared to monocropping, NC, NUE, and NA were significantly increased by 6.51%, 19.38%, and 8.14% in the interplanting system. The extent of their increase was higher under LN treatment than NN treatment. For example, NC and NUE increased by 8.19% and 26.60% in interplanting under LN treatment, but increased by 5.17% and 11.51% under NN treatment. Although N input caused a pronounced increase in NC (from 2.78 g plant⁻¹ under LN treatment to 3.44 g plant⁻¹ under NN treatment) and NA, NUE decreased (from 44.89% under LN treatment to 38.33% under NN treatment).

3.2. Crop Growth in Interplanting

Interplanting promoted corn root and shoot growth. Compared to monocropping, the leaf area index (LAI) (Figure S3), dry matter weight (DMW) (Figure 3a), root dry weight (RDW) (Figure 3b) and dry matter accumulation post anthesis (DMA) (Figure S2) were increased by 4.50%, 5.23%, 6.50%, and 5.22% in interplanting, which resulted in an average increase in grain yield (GY) of 6.81% (Figure 3c). Grain yield was increased significantly with nitrogen application (from 8.98 t ha⁻¹ under the LN treatment to 10.04 t ha⁻¹ under NN treatment), but root dry weight was decreased (from 24.55 g plant⁻¹ under LN treatment to 21.63 g plant⁻¹ under NN treatment). However, the positive effect of interplanting was weakened with nitrogen application. Compared to monocropping, the grain yield was significantly increased by 9.07% under LN treatment and by 4.83% under NN treatment in interplanting.

3.3. Soil Nutrient and Net Potential Mineralization

Interplanting and nitrogen application significantly promoted the consumption of NH₄⁺ and NO₃⁻ in soil (

Table 1. Rhizosphere soil nutrients of two corn cultivars under different nitrogen levels (N) and planting patterns (P) in 2022.

Nitrogen	Planting	SMC	NH4+	NO3−	AP	AK	TN	TP	TK
Level	Pattern	(%)	(mg·kg−1)	(mg·kg−1)	(mg·kg−1)	(mg·kg−1)	(g·kg−1)	(g·kg−1)	(g·kg−1)
LN	M	21.25 ± 0.11 b	5.75 ± 0.08 a	9.4 ± 0.08 a	26.1 ± 0.48 a	196.37 ± 2.76 a	1.29 ± 0.01 a	0.76 ± 0.04 a	4.41 ± 0.19 a
	I	22.25 ± 0.50 a	5.53 ± 0.08 b	8.94 ± 0.21 b	25.73 ± 0.39 a	190.67 ± 2.54 a	1.32 ± 0.04 a	0.74 ± 0.03 a	4.22 ± 0.11 a
NN	M	19.67 ± 0.33 b	8.41 ± 0.08 a	17.44 ± 0.39 a	24.86 ± 0.6 a	191.88 ± 3.57 a	1.34 ± 0.01 a	0.73 ± 0.02 a	4.4 ± 0.12 a
	I	23.13 ± 0.24 a	8.28 ± 0.09 a	15.4 ± 0.24 b	24.71 ± 0.48 a	189.31 ± 3.06 a	1.34 ± 0.01 a	0.71 ± 0.03 a	4.37 ± 0.13 a
ANOVA
N		ns	**	**	**	ns	*	ns	ns
P		**	**	**	ns	ns	ns	ns	ns
N × P		**	ns	**	ns	ns	ns	ns	ns

Based on two-way ANOVA, considering factors of “planting pattern” (2 levels) and “cultivar” (2 levels), different letters indicate significant differences between interplanting (I) and monocropping (M) under the same N level. Based on three-way ANOVA, considering the factors of “N application” (2 levels), “planting pattern” (2 levels), and “cultivar” (2 levels), **, p < 0.01; *, p < 0.05; and ns, p > 0.05 indicate their effects. Soil moisture content (SMC).

). Meanwhile, net potential nitrification and ammonification were also influenced by interplanting (Figure S4). Compared to monocropping, the net potential nitrification rate was increased by 23.57% in interplanting, while the net potential ammonification rate was decreased by 15.02%. Nitrogen application caused an obvious increase in net potential nitrification. The net potential nitrification rate was increased from 0.15 (mg N soil⁻¹ d⁻¹) under LN treatment to 0.27 (mg N soil⁻¹ d⁻¹) under NN treatment, while the net potential ammonification rate was decreased from −0.05 (mg N soil⁻¹ d⁻¹) under LN treatment to −0.07 (mg N soil⁻¹ d⁻¹) under NN treatment (Figure S4).

3.4. Soil Microbial Function of Nitrogen Cycle Pathway

Interplanting had different effects on soil microbial function under LN and NN treatments (Figure 4a). PCOA analysis showed that interplanting and nitrogen application significantly changed the module in the nitrogen cycling pathway of soil microorganisms according to ANOSIM (R = 0.578; p = 0.001) (Figure 4b). Information from KEGG was extracted and also verified the effect of the planting pattern and nitrogen application on the module in the nitrogen cycle pathway. It showed that the signal intensity of the denitrification module increased under two N treatments, while the signal intensities of the dissimilatory nitrate reduction and complete nitrification modules were only increased in LN treatment. The signal intensities of the nitrification, complete nitrification, and methane oxidation modules were enhanced significantly with nitrogen application, while with nitrogen, the signal intensity of the fixation module decreased (Figure 4c).

3.5. Relative Abundance of Genes in Nitrogen Cycle Pathway

Interplanting did have a significant influence on the relative abundance of most nitrogen cycle genes (Figure 5). The amoA, hao and nxrB genes were involved in the nitrification process. In interplanting, the relative abundance of nxrB (especially for sequences related to Actinobacteria phyla) was clearly increased under LN and NN treatments. The relative abundance of amoA (especially for sequences related to Nitrospirae phyla) only increased under NN treatment, which corresponded to an increase in the net nitrification rate (Figure S4). It had negligible effects on the relative abundance of hao in interplanting. The nirK, nirS, norB, and nosZ genes were involved in the denitrification process (Figure 5). The relative abundance of nirK (especially for sequences related to Proteobacteria phyla) was increased from 1507.33 in monocropping to 1932.17 in interplanting. Meanwhile, the relative abundance of norB and nosZ was similar to that of nirK. In the assimilatory nitrate reduction module, the relative abundance of narB (especially for sequences related to Bacteroidetes phyla) and nirA (especially for sequences related to Actinobacteria, Proteobacteria, and Acidobacteria phyla) were increased in interplanting (Figure 5). In the dissimilatory nitrate reduction module, the relative abundance of narG (especially for sequences related to Actinobacteria phyla) was increased in interplanting, while the relative abundance of nrfA (especially for sequences related to Proteobacteria, Candidatus, and Rokubacteria phyla) was decreased. Moreover, there was a stronger influence on the relative abundance of narG and nrfA under LN treatment. In addition, the nifH gene was sporadically detected in soil samples with a decreasing relative abundance, corresponding to the nitrogen fixation rate reducing under NN treatment (Figure S4).

3.6. Relationships between Plant Nitrogen Content, Microbial Nitrogen Cycling, and NUE

Interplanting significantly improved aboveground growth, resulting in enhanced root growth, nitrification, and denitrification. Root growth was negatively related to the mineral nitrogen content. The plant nitrogen content was positively affected by the mineral nitrogen content and nitrification. NUE was positively affected by nitrification (Figure 6).

4. Discussion

4.1. Interplanting Promoted Plant Growth and Nitrogen Utilization

Compared with the monocropping system, the use of an appropriate intercropping population can fully utilize species diversity [38] and give rise to the effects of spatiotemporal niche complementarity and interspecific promotion on the growth of different crops [39]. In this study, we found that there were also complementary effects on nitrogen utilization resulting from the interplanting of cultivars with different nitrogen use efficiencies, which improved corn growth and grain yield. Previous studies confirmed that the interplanting of corn cultivars could optimize the canopy structure and increase the light use efficiency, leaf area index, and biomass, indicating that interplanting increased plant growth and the photosynthetic products allocated to roots [40,41]. Meanwhile, the root morphological characters of plants had the ability to self-regulate according to adjacent plants and attained an optimal distribution in the roots [42]. In this study, the root biomass was increased in ZD958 and DH3622 interplanting (Figure 3b), which is consistent with our previous studies on improving root biomass through the trait-based interplanting of two corn cultivars [43]. However, some results also suggested that legume and corn intercropping reduced the root biomass of corn [44]. The functional mechanism of root growth was inconsistent between intercropping and interplanting. In a corn and legume intercropping system, the mechanism was that nitrogen deficiency was alleviated through legume rhizobium biological nitrogen fixation under low-nutrient soil, and nitrogen absorption and the utilization of adjacent gramineous crops were promoted [45,46]. However, in corn interplanting, competitive root growth likely led to increased root biomass, which promoted the elongation and vertical distribution of roots in deep soil for acquiring nutrients. The same was also confirmed in a study on the interplanting of two corn cultivars with different root angles [43]. Meanwhile, this study also showed that root biomass in the interplanting system was inhibited with excessive nitrogen application, but it was promoted under low nitrogen application (Figure 3b). This is consistent with the negative relationship identified between root biomass and nitrogen application when the nitrogen was saturated in the soil [47].

4.2. Interplanting Enhanced Nitrogen Accumulation and Nitrogen Cycling Function in Rhizosphere

Similarly to plant growth promotion, interplanting increased plant nitrogen accumulation and reduced the content of NO₃⁻ and NH₄⁺ in the rhizosphere (Table 1). This was consistent with the reduced mineral nitrogen content found in maize intercropped with soybeans [48]. When maize was intercropped with soybeans, the two crops competed differently for NO₃⁻ and NH₄⁺ due to their different root systems. The highly competitive ability of maize leads to a lower rhizosphere mineral nitrogen content in soybeans, thus stimulating nitrogen fixation in this crop [49]. Meanwhile, interplanting decreased the NO₃⁻/NH₄⁺ ratio in the present study. This indicated that a greater number of protons were secreted into the rhizosphere, leading to rhizosphere acidification. This result is consistent with the intercropping of maize with legumes [49]. Changes in other root exudates would further alter rhizosphere microbes.

In general, certain functional genes of soil microorganisms are composed of different species, and their relative abundance is determined through the dynamics of numerous species in soil microbial communities [50,51]. Excessive N application has been shown to cause soil degradation, such as soil aggregate destruction and acidification, which directly reduces soil microbial diversity [3,52]. Therefore, nitrogen application can significantly affect the abundance of microorganisms related to nitrogen cycling [8]. Our results show that Nitrospirae is the main nitrifying bacteria in soil. It directly affected the abundance of the amoA and hao genes through nitrification (Figure 5), which was consistent with previous results [53]. A higher nitrogen content in soil will accelerate the rate of the N cycle. During the nitrification and denitrification process, the relative abundances of amoA, hao, nxrB, nirK, norB, and nosZ were increased with nitrogen application (Figure 5). Ammonium and nitrate were shown to be the main factors affecting the nitrification and denitrification process [51], which is consistent with our results. Our study indicated that the soil N cycle was closely related to nitrogen application (Figure 5). Nitrogen application promoted plant growth, which strengthened the interaction between the roots and soil microorganisms [4]. Changes in these functional microbes may cause nitrogen loss. Research has shown that nitrogen application has significantly increased the abundance of amoA, nirK and nirS [54]. Excessive nitrogen application reduced the abundance of the nifH gene and increased the relative abundance of Proteobacteria, which was closely related to the denitrification module [8]. Our results showed that Nitrospirae and Proteobacteria were increased under NN treatment, which increased the gene abundance of amoA and hao in the nitrification module. Additionally, increased Proteobacteria were dominant in denitrification (Figure 5).

Microorganisms demonstrate an extremely sensitive response to environmental changes, so microbial functions are altered with changes in crop types, soil textures, planting patterns, or other environmental factors [55]. Our research testified that interplanting had a significant impact on soil microbial functions and increased the relative abundance of genes in the nitrification, denitrification, and nitrate/nitrite reduction pathways (Figure 4), indicating more aggressive soil nitrogen cycling. Interactions between the roots and rhizosphere may induce nitrogen-involved microbes and promote the nitrogen cycle [56,57]. The increased abundance of nitrogen cycle functional genes contributed to lower mineral nitrogen losses and a lower rhizosphere mineral content in maize and soybean strip intercropping [48,58]. The enhanced nitrate/nitrite reduction also decreased nitrate nitrogen and further increased nitrogen retention [59].

5. Conclusions

Interplanting between high-nitrogen-use-efficiency and low-nitrogen-use-efficiency cultivars improved shoot and root growth and increased yield, nitrogen accumulation, and nitrogen use efficiency. Interplanting accelerated the consumption of soil nitrogen components, shifted microorganism N cycle function, and enhanced nitrification. Intraspecific intercropping with complementary root or rhizosphere functional traits needs to be further studied. This will allow its common mechanisms to be explored, allowing for improvements in nutrient utilization, stress resistance, grain yield, and climate change adaptation.