Contrasting Rhizosphere Soil Stoichiometric Traits and Microbial Nitrogen Limitation between Maize and Peanut under Intercropping and Straw Retention

Contrasting Rhizosphere Soil Stoichiometric Traits and Microbial Nitrogen Limitation between Maize and Peanut under Intercropping and Straw Retention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contrasting Rhizosphere Soil Stoichiometric Traits and Microbial Nitrogen Limitation between Maize and Peanut under Intercropping and Straw Retention qila sa, Wei Qi, Jie Liang, YuJun Cao, FanYun Yao, YongJun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8696775/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Extracellular enzyme stoichiometry is a key indicator for assessing resource limitations faced by soil microorganisms. Yet the characteristics of microbial resource limitation in rhizosphere soil under the combined agricultural practices of intercropping and straw retention remain unclear. Methods Here, we conducted a field experiment in the black soil region of Northeast China, to quantify the effects of intercropping and straw retention on soil nutrients, microbial biomass, extracellular enzyme activities, and their C:N:P stoichiometry in the rhizosphere of maize and peanut crops. Results Our results revealed an average vector length (VL) of 1.68 and 1.57 for extracellular enzymes in the rhizosphere soil of maize and peanut, with a vector angle (VA) of 37.80° and 34.67°, respectively. This indicated that soil microorganisms in the rhizosphere of both crops were co-limited by C and N, and the N limitation was more significant in the peanut rhizosphere. Notably, the combined treatment of intercropping and full straw retention increased the VA by 5°, effectively alleviating N limitation in the rhizosphere soil. The extracellular enzyme C:N:P stoichiometry in the rhizosphere soil of maize and peanut was 1.33:1.29:1.00 and 0.89:1.29:1.00, respectively. Microbial biomass nitrogen (MBN) was the primary factor affecting microbial nutrient limitation. Conclusions The extracellular enzyme stoichiometric characteristics of rhizosphere soil differed significantly between the two crops. Intercropping had a stronger impact on rhizosphere microbial nutrient limitation than straw retention, and their synergistic effect could significantly alleviate rhizosphere microbial N limitation by enhancing extracellular enzyme activity. C:N:P stoichiometry Extracellular enzyme activity Microbial biomass Microbial nutrient limitation Soil nutrients Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights Rhizosphere soil’s extracellular enzyme stoichiometric characteristics varied significantly between different crops Microbial biomass nitrogen is a key factor affecting microbial nutrient limitation in rhizosphere soil Intercropping with straw retention alleviates nitrogen limitation, but intercropping has a stronger effect Introduction In China, the black soil region of its Northeast is a core grain production base, with irreplaceable strategic significance for national food security. In recent years, however, affected by factors such as soil erosion, long-term intensive cultivation, and poor nutrient management, this region has generally faced several concurrent problems, including thinning of the plow layer, decreased organic matter content, and soil function degradation (Wang et al, 2021 ). In order to address and remedy this situation, it is imperative to explore more sustainable soil management models. As a proven and efficient agricultural management practice, intercropping has been widely applied around the world because of its advantages at improving soil quality and enhancing resource use efficiency (Ma et al, 2022 ). In particular, the maize-peanut intercropping system is capable of optimizing the composition and diversity of soil microbial community structure, which promotes soil health (Zhao et al, 2022 ). Meanwhile, straw retention, another important practice in farming, can effectively enhance soil microbial activity by returning organic matter and nutrients to the soil, which promotes nutrient cycling and material transformation in agroecosystems (Wang et al, 2018 ), providing important support for ensuring high crop yields and sustainable farmland utilization. Soil microorganisms, as core biological drivers of nutrient cycling, often have their metabolic activities limited by the local availability of certain soil nutrients (Cui et al, 2019 ; Moorhead et al , 2016). Accordingly, soil extracellular enzyme stoichiometry provides a fresh perspective for studying those kinds of microbial nutrient limitation. By analyzing the activities of key enzymes involved in microbial acquisition of carbon (β-1,4-glucosidase, BG ), nitrogen (β-1,4-N-acetylglucosaminidase, NAG; leucine aminopeptidase, LAP), and phosphorus (acid or alkaline phosphatase, AP), as well as their stoichiometric ratios (BG:(NAG + LAP) :AP), this method can accurately reflect the nutrient demands and utilization strategies of soil microbial communities (Liu et al, 2023 ; Sinsabaugh et al, 2009 ; Zuccarini et al, 2020 ). Many studies have shown that these enzyme activity ratios are closely related to soil C:N:P stoichiometric characteristics and can serve as effective indicators for assessing microbial nutrient limitation (Peng et al, 2016 ; Yang et al, 2020 ; Yao et al, 2023 ). The rhizosphere, as the active interface zone for soil–plant interactions, is far richer in microbial and extracellular enzyme activities than surrounding bulk soil (Gartner et al, 2012 ). It is known that extracellular enzyme stoichiometric characteristics of the rhizosphere are affected not only by soil physical properties, but are also closely related to local vegetation types and land management practices (Cui et al, 2018 ). Over the last few years, the benefits of straw retention and maize-legume intercropping for the rhizosphere microecosystem have been confirmed, in that both practices can substantially increase microbial diversity and soil nutrient availability (Jiang et al, 2024 ; Song et al, 2022 ). Recently, we reported on how these two agricultural practices affect extracellular enzyme activities in bulk soil (Yao et al, 2025 ). However, critical aspects of microbial nutrient limitation in rhizosphere soil and their regulatory mechanisms under the combined management model of intercropping and straw retention remain unclear, which precludes a comprehensive understanding of their ecological effects. Using the enzyme eco-stoichiometry approach, this study focused on exploring the effects of maize-peanut intercropping and straw retention on microbial nutrient limitation in rhizosphere soil. We address three pertinent scientific questions: (1) How do rhizosphere soil properties, microbial biomass, extracellular enzyme activities, and stoichiometric ratios of maize and peanut respond to the combined practices of intercropping and straw retention? (2) What is the status of rhizosphere soil microbial nutrient limitation of maize and peanut under those different practices? (3) What are the main factors driving rhizosphere soil’s nutrient limitation of maize and peanut crops? Answering these timely questions can provide a theoretical basis for the sustainable management of farmland ecosystems in China’s vital black soil region. Materials and methods Site description This study was conducted at the Halahai Comprehensive Experimental Station of Jilin Academy of Agricultural Sciences (44°05′N, 124°51′E), where a long-term, fixed-field experiment platform for maize-peanut intercropping (with rotation in the following year) was established in 2015. The study area has a temperate continental monsoon climate, with an average annual temperature of 4.7°C and an average annual precipitation of 507.7 mm. The tested soil is chernozem with a loamy texture. The 0–20 cm plow layer has these soil fertility properties: alkaline hydrolyzable nitrogen: 47.37 mg/kg, available phosphorus: 13.27 mg/kg, available potassium: 173.68 mg/kg, organic matter: 13.79 g/kg, and pH = 7.86. Experimental design This study used split-plot design with blocking. In this hierarchical design (two levels), the main plot stratum corresponded to three planting patterns: maize||peanut 6:6 equal-width intercropping (row ratio of 1:1), peanut sole cropping, and maize sole cropping; the subplot stratum consisted of two treatments: full straw retention (100%) and root stubble retention (only root stubble retained). Four treatment combinations were selected for analysis: maize||peanut with root stubble retention (I0), maize||peanut with full straw retention (I100), sole cropping with root stubble retention (S0, control), and sole cropping with full straw retention (S100). A total of 9 main plots were set (= 3 replicates×3 planting patterns), each intercropping treatment was set as 120 m × 3.9 m, and each sole cropping treatment was set as 120 m × 10.4 m. The two tested crops were the semi-compact maize variety ‘Fumin 985’ and the early-maturing upright-flowering peanut variety ‘Huayu 20’, with a unified row spacing of 0.65 m. Maize was sown on 7 May 2024, at a planting density of 60 000 plants·ha − 2 . Compound fertilizer (N:P 2 O 5 :K 2 O = 15:15:15) was applied as base fertilizer at a rate of 970 kg·ha − 1 , and urea (N content: 46%) was top-dressed at the large bell stage at a rate of 163 kg·ha − 1 . Peanut was sown on 13 May 2024, at a planting density of 120 000 holes·ha − 2 (2 seeds per hole). Compound fertilizer (N:P 2 O 5 :K 2 O = 12:17:16) was applied as base fertilizer at a rate of 700 kg·ha − 1 , with no top-dressing during the growth period. Straw treatment was conducted after maize harvest in early October: for the root stubble retention treatment, the aboveground straw of maize was completely removed; for the full straw retention treatment, the straw was crushed to about 10 cm and plowed into the soil. Soil sample collection Soil samples were collected during the milk-ripe stage of maize and the pod-filling stage of peanut. To obtain rhizosphere soil, we used the soil-shaking method and followed these steps: from each plot, three maize and six peanut plants were selected as representative individuals, with a 30-cm × 30-cm × 30-cm root zone soil block excavated per crop type. After carefully removing any surface debris, the loose soil was removed by gentle shaking, and the soil adhering to within 5 mm of the surface of fine roots (diameter < 2 mm) was collected as the rhizosphere samples. These samples were then fully mixed and passed through a 2-mm sieve to remove any residual roots and other impurities. Next, each composite sample was immediately divided into two parts for processing. One part was quickly frozen with liquid nitrogen and stored in an ultra-low temperature refrigerator at − 80°C for the determination of microbial biomass carbon (MBC), biomass nitrogen (MBN), biomass phosphorus (MBP), and extracellular enzyme activities related to C, N, and P cycling. The other part was naturally air-dried for the analysis of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents. Soil sample measurements Standard methods were used to determine the basic physical and chemical properties of soil. The SOC content was quantified with a total organic carbon analyzer. The TN content was measured via the Kjeldahl method, with these specific steps: soil samples were digested with concentrated H 2 SO 4 until they appeared clear, then volumetrically fixed, and distilled and titrated using a Kjeldahl nitrogen analyzer (Bao, 2000 ; Bremner, 2009 ). The TP content was quantified by the H 2 SO 4 -HClO 4 digestion-molybdenum antimony anti-colorimetry method: after complete digestion, the volume was fixed, the supernatant was taken, molybdenum antimony anti-color developer was added, and colorimetry performed at a 700-nm wavelength (Murphy et al , 1962). Microbial biomass was determined using the chloroform fumigation-extraction method (Margesin et al , 2005). During chloroform fumigation, to kill living soil microorganisms as completely as possible, soil samples were placed in Petri dishes, put into a desiccator, and the fumigation time in chloroform vapor extended from 24 h to 48 h. For the MBN content’s determination, after extraction with 0.5 mol·L − 1 K 2 SO 4 solution, the carbon-nitrogen ratio content was determined by high-temperature oxidation in a total organic carbon analyzer. When calculating the MBC:MBN content, the conversion coefficients used for the interpolation of C, N, and P contents between the fumigated and non-fumigated soils were 0.45, 0.54, and 0.40, respectively (Mueller. 1996). The respective activity of four extracellular enzymes related to C, N, and P cycling, namely β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG) and leucine aminopeptidase (LAP), and alkaline phosphatase (AP), were determined using the microplate fluorometric method (German et al, 2011 ). All sample preparations for these extracellular enzyme activity determinations were completed in advance. Once prepared, the soil previously stored frozen (at − 80°C) was transferred to a 4°C environment for thawing one day prior. Taking six soil samples as a group, 1.5 g of soil was accurately weighed and placed in a hard glass bottle, to which 150 mL of buffer solution was added, and the mixture then fully stirred for 2 min (using a stirrer). Next, the soil mixture was transferred to a small bowl through a filter, poured into a rotor, and the reaction solution slowly added to it under continuous stirring (with a magnetic stirrer). Finally, enzyme activity was recorded using a multi-functional microplate reader. These determinations were made for three replicates per enzyme, with enzyme activity expressed as the amount of product produced per unit mass of soil per unit time. Microbial nutrient limitation Based on the stoichiometric characteristics of the above extracellular enzyme activities, a vector analysis model was applied to evaluate the nutrient limitation status of soil microorganisms (Moorhead et al . 2023; Sinsabaugh et al, 2012). First, the respective activity values of BG, NAG, LAP, and AP were transformed to their natural logarithm (ln), and enzyme activity ratios then calculated this way: Enzyme C:N=ln（BG）/ln（NAG+LAP）........................ （1） Enzyme C:P=ln（BG）/ln（AP）...................................... （2） Enzyme N:P=ln（NAG+LAP）/ln（AP）......................... （3） Here, enzyme C:N refers to the soil enzyme carbon-to-nitrogen ratio; enzyme C:P refers to the carbon-to-phosphorus ratio; and enzyme N:P refers to the nitrogen-to-phosphorus ratio. Vector analysis was used to quantify the degree of microbial nutrient limitation, as follows: VL = SQRT（X 2 +Y 2 ）....................................................... （4） VA = Degrees [ATAN2（X,Y）]......................................... （5） In the above two equations, X = ln (NAG + LAP) / ln (AP) and Y = ln (BG) / ln (NAG + LAP); SQRT denotes the square root function; Degrees is the function for converting radians to degrees; and ATAN2 is the arctangent function. A longer vector length (VL) indicates a greater degree of C limitation on soil microorganisms; a vector angle (VA) > 45° means microorganisms are limited by P, while a VA < 45° indicates N limitation (Moorhead et al , 2015). Recent studies have suggested using optimal theoretical thresholds (0.61° and 55°) as robust criteria for distinguishing microbial N and P limitations (Cui et al , 2024). Statistical analyses All empirical data were initially organized using MS Excel 2021, with their statistical analysis performed in SPSS 18.0 software. Three-way ANOVAs (analysis of variance) were used to test the effects of crop species, treatment methods, and their interactions on rhizosphere soil nutrient characteristics, microbial biomass (C, N, P), and extracellular enzyme activity indicators. Redundancy analysis (RDA) and Pearson correlations were used to reveal the relationships among various indicators. Vector analysis diagrams and heatmaps were drawn in Origin Pro 2022 software, and the data presented as the mean ± standard error (n = 3). Statistical analyses All empirical data were initially organized using MS Excel 2021, with their statistical analysis performed in SPSS 18.0 software. Three-way ANOVAs (analysis of variance) were used to test the effects of crop species, treatment methods, and their interactions on rhizosphere soil nutrient characteristics, microbial biomass (C, N, P), and extracellular enzyme activity indicators. Redundancy analysis (RDA) and Pearson correlations were used to reveal the relationships among various indicators. Vector analysis diagrams and heatmaps were drawn in Origin Pro 2022 software, and the data presented as the mean ± standard error (n = 3). Results Rhizosphere soil’s physicochemical properties and nutrient stoichiometry As Table 1 shows, the planting pattern (intercropping vs. sole cropping) significantly affected the rhizosphere contents of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) ( P < 0.01). Specifically, compared with both sole cropping treatments (S0 and S100), both intercropping treatments (I0 and I100) increased the SOC content by 6.21% and 13.57%, the TN content by 8.57% and 12.49%, and the TP content by 12.01% and 40.29% in the maize and peanut rhizospheres, respectively. Notably, combining the intercropping and full straw retention (I100) practices had a more significant promoting effect: it increased the TN content in the maize rhizosphere by 11.41% relative to the control (S0), and increased the TP content in the maize and peanut rhizospheres by 22.81% and 43.41%, respectively (Fig. 1). However, the straw retention practice (root stubble retention/full straw retention) did not significantly affect the SOC, TN, or TP content, but did change the SOC:TN ratio significantly. Also, the rhizospheric TN content differed significantly between the two crops ( P < 0.01), being generally lower in the peanut than maize rhizosphere. Stoichiometric analysis showed that both intercropping and straw retention significantly lowered the TN:TP and SOC:TP ratios, with a significant interaction effect of crop species and planting pattern on the either ratio ( P < 0.05). Table 1 Results of ANOVAs assessing the main effects and interactions for planting pattern (PP), residue retention (RR), and Crop species (CS) on rhizosphere soil SOC, TN, TP contents and their stoichiometric ratios. Factor SOC TN TP SOC:TN SOC:TP TN:TP PP 9.77** 20.452** 27.123*** 0.110n.s. 17.890** 19.389** RR 4.019n.s. 0.186n.s. 1.444n.s. 6.164* 12.516** 4.818* CS 0.019n.s. 3.757** 1.163n.s. 4.044n.s. 3.587n.s. 0.730n.s. CS×PP 1.227n.s. 0.416n.s. 6.231* 0.644n.s. 6.302* 10.541* CS×RR 0.053n.s. 2.419n.s. 0.285n.s. 1.820n.s. 0.526n.s. 0n.s. PP×RR 2.015n.s. 0.449n.s. 0.900n.s. 1.342n.s. 0.048n.s. 0.921n.s. CS×PP×RR 0.297n.s. 0.186n.s. 0.127n.s. 1.688n.s. 0.080n.s. 1.431n.s. Note: Bold type indicates P < 0.05, while *, **, and *** corresponding to P < 0.05, P < 0.01, and P < 0.001, respectively. n.s. indicates no significant effect at the P < 0.05 level. The values are F -test statistic. The same below. Microbial biomass and stoichiometry of rhizosphere soil Planting pattern and crop species had significant effects on the microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) contents of rhizosphere soil (Table 2 ). Intercropping significantly increased MBC in the peanut rhizosphere ( P < 0.05). Compared with both sole cropping treatments (S0 and S100), intercropping increased MBC by 0.20% and 35.72%, MBN by 13.43% and 38.11%, and MBP by 14.06% and 50.94% in the maize and peanut rhizospheres, respectively. This indicated that intercropping’s positive impact on microbial biomass in the peanut rhizosphere surpassed that in the maize rhizosphere (Fig. 2). Stoichiometric analysis of microbial biomass showed that, in the maize rhizosphere, the MBC:MBN, MBC:MBP, and MBN:MBP ratios were 8.01 ~ 9.82, 16.92 ~ 23.84, and 2.12 ~ 2.66, respectively, while in the peanut rhizosphere the corresponding ratios were 10.96 ~ 15.29, 19.38 ~ 23.10, and 1.39 ~ 1.93. The MBC:MBN ratio was significantly affected by crop species, the interaction between planting pattern and straw retention method ( P < 0.05), as well as the three-way interaction among those factors ( P < 0.05); the MBN:MBP ratio was affected chiefly by crop species ( P < 0.01); while the MBC:MBP ratio was similar among different treatments. When compared with the control (S0) treatment, the combination of intercropping and full straw retention (I100) decreased the MBC:MBN ratio by 22.64% in maize rhizosphere soil, but increased it by 5.20% in the peanut rhizosphere. This further confirmed the divergent responses of microbial communities in different crop rhizospheres to management practices. Table 2 Three-way ANOVAs testing the effects of different treatments on rhizosphere soil contents of MBC, MBN, MBP and their stoichiometric ratios. Factor MBC MBN MBP MBC: MBN MBC: MBP MBN: MBP pp 4.843* 10.261** 14.058** 0.994n.s. 0.936n.s. 0.021n.s. RR 0.656n.s. 1.898n.s. 2.986n.s. 0n.s. 0.719n.s. 0.876n.s. CS 11.055** 85.100*** 10.437** 40.211*** 0.013n.s. 14.2963** CS×PP 4.688* 0.314n.s. 2.543n.s. 0.620n.s. 0.006n.s. 0.153n.s. CS×RR 0.103n.s. 0.354n.s. 3.919n.s. 1.179n.s. 3.679n.s. 1.190n.s. PP×RR 4.368n.s. 0.001n.s. 1.447n.s. 9.148** 0.350n.s. 0.529n.s. CS×PP×RR 0.103n.s. 1.403n.s. 1.230n.s. 7.190* 0.411n.s. 2.767n.s. Extracellular enzyme activities of rhizosphere soil and their stoichiometric characteristics As Fig. 3 shows, intercropping significantly bolstered the extracellular enzyme activities in rhizosphere soil (Table 3 , P < 0.01). The activity range of the carbon-acquiring enzyme (BG), nitrogen-acquiring enzymes (NAG + LAP), and phosphorus-acquiring enzyme (AP) in the maize rhizosphere were 22.10 ~ 44.62, 25.65 ~ 34.23, and 11.77 ~ 16.68 µmol·g − 1 ·d − 1 , respectively, while the corresponding enzyme activities in peanut rhizosphere were 15.36 ~ 28.81, 25.39 ~ 36.06, and 8.60 ~ 14.20 µmol·g − 1 ·d − 1 . Compared with sole cropping (S0 and S100), intercropping (I0 and I100) increased BG activity by 63.68% and 64.99%, AP activity by 34.53% and 49.51%, and NAG + LAP activity by 18.44% and 29.62% in the maize and peanut rhizospheres, respectively. As well, BG and AP activities were significantly lower in the peanut than maize rhizosphere ( P < 0.01). However, applying intercropping and straw retention in combination (I100) enhanced enzyme activities more so than using either treatment alone, increasing BG activity by 94.23% and 70.34% in the maize and peanut rhizospheres, respectively. We detected significant differences in the C, N, P stoichiometric ratios of extracellular enzymes in the rhizosphere between the two crop types ( P < 0.01). Intercropping augmented the BG:(NAG + LAP) ratio by 36.52% and 27.22%, and the BG:AP ratio by 21.36% and 11.59%, in the maize and peanut rhizospheres, respectively. Both BG activity and the BG:AP ratio were significantly affected by the combined practice of intercropping and straw retention ( P < 0.05), while straw retention method alone had no discernible effect on enzyme activities. Table 3 Three-way ANOVAs testing the effects of different treatments on BG, NAG + LAP, AP activities and their stoichiometric ratios in rhizosphere soil. Factor BG NAG + LAP AP BG:(NAG + LAP) (NAG + LAP):AP BG:AP PP RR CS CS×PP CS×RR PP×RR CS×PP×RR 53.967*** 0.992n.s. 48.336*** 2.676n.s. 1.015n.s. 4.569* 0.570n.s. 15.934** 1.420n.s. 0.n.s. 0.654n.s. 1.010n.s. 1.613n.s. 0.126n.s. 33.078*** 0.001n.s. 20.203** 0.008n.s. 0.881n.s. 1.107n.s. 0.334n.s. 29.354*** 0.161n.s. 81.120*** 3.606n.s. 0.088n.s. 3.606n.s. 1.894n.s. 4.248n.s. 0.952n.s. 18.711** 0.030ns. 0.002n.s. 0.060n.s. 0.006n.s. 13.751** 3.539n.s. 20.450*** 1.792n.s. 0.144n.s. 4.712* 2.254n.s. Vector analysis of extracellular enzyme activities in rhizosphere soil The vector analysis model uncovered significant differences in the stoichiometric characteristics of extracellular enzymes under the different treatments and crop types (Fig. 4 a, b). In both maize and peanut rhizosphere soils, the vector length (VL) of each extracellular enzyme was greater than 1.46, with vector angles (VA) smaller than 45°, indicating that soil microorganisms in the study area were simultaneously C- and N-limited. The I100 treatment had the largest VL value, indicating the highest degree of carbon limitation, and its VA value was also the largest, implying significantly alleviated nitrogen limitation; in contrast, the S100 treatment had a smaller VL value, with relatively weaker carbon limitation. ANOVA results showed that planting pattern and crop species had significant main effects on microbial metabolic characteristics ( P < 0.01), along with a significant interaction effect of planting pattern and residue retention on VL (Table 4 ). Specifically, compared with either sole cropping treatment (S0 and S100), intercropping (I0 and I100) significantly increased the VL by 2.10% in the maize rhizosphere. Further analysis of the ln(BG):ln(NAG + LAP) and ln(BG):ln(AP) ratios confirmed that all treatments were in the realm of carbon and nitrogen limitations (Fig. 4 c); however, the degree of nitrogen limitation faced by microorganisms in the peanut rhizosphere (average VA = 34.67°) was significantly stronger than that in the maize rhizosphere (average VA = 37.80°), which was highly consistent with the earlier vector analysis results. There was a significant positive correlation between VL and VA in both maize and peanut rhizospheres ( P < 0.05) (Fig. 4 d), which may indicate that carbon input practices such as straw retention, while alleviating carbon limitation (shorter VL), nonetheless intensify microbial competition for nitrogen (smaller VA means stronger nitrogen limitation). Therefore, in actual field production settings, nitrogen application usually needs to be supplemented simultaneously after straw retention to balance carbon and nitrogen. Table 4 Three-way ANOVAs testing the effects of different treatments on the vector length (VL) and vector angle (VA) of extracellular enzyme activities and extracellular enzymatic stoichiometric ratios in rhizosphere soil. Factor VL VA PP 14.038** 9.338** RR 3.683n.s. 0.346n.s. CS 37.486*** 23.554*** CS×PP 1.203n.s. 0.266n.s. CS×RR 0.033n.s. 0.035n.s. PP×RR 5.645* 0.059n.s. CS×PP×RR 1.879n.s. 0.020n.s. Correlation analysis between soil nutrients and microbial biomass, and the C:N:P stoichiometric ratios of extracellular enzymes The Pearson correlations revealed close relationships between soil properties and microbial metabolic characteristics (Fig. 5 ). In the maize rhizosphere, soil BG activity showed significant or highly significant positive correlations ( P < 0.05) with TN, TP, and MBCMBN. The activity of NAG + LAP was significantly positively correlated ( P < 0.05) with TN as well as MBN. The AP activity had strong, significant positive correlations ( P < 0.01) with TN, TP, and MBC:MBN. Regarding VL, its correlation with TP and TN:TP was positive and significant ( P < 0.05). In the peanut rhizosphere, more significant correlations were observed: the BG, NAG + LAP, and AP activities all had highly significant positive correlations ( P < 0.01) with SOC, TN, TP, MBC, MBP, and TN:TP, SOC:TP ratios, and a significant positive correlation ( P < 0.05) with MBN:MBP. The activity of AP had as significant positive correlation ( P < 0.05) with MBN. Finally, VL showed a significant positive correlation ( P < 0.05) with SOC:TP, while VA had highly significant positive correlations ( P < 0.01) with SOC, TN, TP, and MBP, and a significant positive correlation ( P < 0.05) with MBC. To assess the effects of SOC, TN, TP, microbial biomass C, N, P, and their stoichiometric ratios on microbial nutrient limitation in rhizosphere soil, we used RDA to examine relationships between relevant indicators and VL, VA under the contrasting planting patterns and straw retention methods (Fig. 6 ). These results showed that soil physicochemical properties, microbial biomass, and their stoichiometric characteristics together explained 79.51% of the variation in microbial nutrient limitation; among them, three factors—MBN (54.6%), MBC:MBN (5.1%), and TN (8.6%)—best explained the changes in VL and VA in rhizosphere soil. Both VL and VA were distributed in the same direction as rhizosphere soil SOC, TN, TP, MBC, MBN, MBP, and MBN:MBP, showing positive correlations with small angles and long arrows; conversely, both featured negative correlations with SOC:TN, SOC:TP, TN:TP, MBC:MBP, and MBC:MBN. This confirms the core role of microbial biomass and its stoichiometric characteristics in influencing nutrient limitation in rhizosphere soils of crops in the study area, especially the high explanatory power of MBN. Discussion Effects of intercropping and straw retention on rhizosphere soil nutrients, microbial biomass, and their stoichiometric characteristics Intercropping and straw retention are key agricultural practices that can help to regulate rhizosphere soil nutrients and stoichiometric characteristics. The results of this study show that the maize-peanut intercropping system significantly improved the rhizosphere soil’s nutrient status via multiple mechanisms. Intercropping not only increased the amount of organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) in rhizosphere soil, but also, and more importantly, it achieved a synergistic improvement of nutrients by promoting the proliferation of nitrogen-fixing and phosphorus-solubilizing bacteria in the rhizosphere (Zan et al, 2023 ), increasing root exudates and residue inputs (Zhao et al, 2023 ), and optimizing soil microbial community structure (Zhu et al, 2024 ; Ma et al, 2025 ). In contrast, we find that straw retention mainly increased the carbon input and intensified microbial competition for nitrogen and phosphorus, thereby disrupting the original soil C:N:P balance and leading to significant changes in the soil carbon, nitrogen, and phosphorus ratios (Li et al, 2023 ). This pronounced discrepancy highlights the distinct regulatory mechanisms of different agricultural practices upon soil nutrient cycling dynamics. Our field experimental results confirm that soil ecological stoichiometric characteristics can serve as sensitive indicators for assessing the effects of farmland management measures (Yang et al, 2021 ). Maize-peanut intercropping significantly increased the contents of microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) in rhizosphere soil, but there were marked differences how they responded under the two crop types. We found a stronger response of microbial biomass to intercropping in the peanut rhizosphere than in the maize rhizosphere, a result closely tied to the unique root nodule nitrogen-fixing system of legumes and the microbial microenvironment shaped by specific root exudates (Li et al, 2016 ). Moreover, in the present study, the average MBC:MBN ratio in all treatments was above 7, this indicating that fungi dominated the microbial community in the study area (Hessen et al, 2004 ). Yet the combined treatment of intercropping and straw retention decreased the MBC:MBN ratio in maize rhizosphere, indicating a shift in microbial community towards bacterial dominance (Guan et al, 2023 ; Zhao et al, 2022 ). Notably, the lower MBN:MBP ratio in the peanut rhizosphere implies that nitrogen limitation is stronger there (Cui et al, 2024 ; Liu et al, 2024 ), providing new evidence for better understanding the differences in rhizosphere microecology between legume and non-legume crops. These results deepen our knowledge of pivotal aspects of microbial nutrient limitation in the rhizosphere of different crops and offer a theoretical basis for precision agricultural management. Effects of intercropping and straw retention on extracellular enzyme activities in rhizosphere soil and their stoichiometric characteristics This study found that maize-peanut intercropping significantly enhanced the activities of a carbon-acquiring enzyme (BG), nitrogen-acquiring enzymes (NAG + LAP), and a phosphorus-acquiring enzyme (AP), with nutrient-specific mechanisms. That is, intercropping increased the input of soil organic matter into the rhizosphere, likely providing sufficient carbon sources for microorganisms to bolster the activity of BG (Wang et al , 2022); it also improved nitrogen availability and optimized microbial community structure, driving the organic N-mineralization process mediated by NAG + LAP (Ba et al, 2022 ). Meanwhile, the soil TP content also increased under intercropping, with AP activity being notably strengthened (Qu et al, 2023 ). We also found that the BG and NAG + LAP activities in maize rhizosphere soil exceeded those in peanut rhizosphere soil, but vice versa for the activity of acid phosphatase (AP), suggesting crucial differences exist in the extracellular enzyme stoichiometric characteristics of different crops’ rhizosphere soils. In this study, the extracellular enzyme C:N:P ratios in maize and peanut rhizosphere soils were 1.33:1.29:1.00 and 0.89:1.29:1.00, respectively, this deviating noticeably from the global average soil enzyme C:N:P ratio of approximately 1:1:1 (L et al, 2008). This discrepancy may be attributable to the high organic matter content in the black soil of the maize-growing area, where microorganisms must bolster their carbon-decomposing enzymes to meet their metabolic needs. On the other hand, the BG:(NAG + LAP) ratio in the peanut area likely lined to the low BG activity and a carbon-nitrogen coupling effect caused by nitrogen fixation by that legume plant. This finding provides new evidence for understanding the adaptive differentiation of nutrient acquisition strategies in rhizosphere microorganisms of different crops (Ma et al, 2024 ). Effects of intercropping and straw retention on the characteristics of microbial resource limitations in rhizosphere soil Microbial resource limitations constitute a fundamental link connecting soil, microorganisms, and plant crops, whose status at given place and time directly determines the nutrient use efficiency, soil health, and sustainability of farmland (Sinsabaugh et al, 2009 ). Using the vector analysis model (Moorhead et al , 2016; Cui et al, 2021 ), our study revealed that microorganisms in both maize and peanut rhizospheres are limited by both carbon and nitrogen, but crop-specific differences were evident. This finding is in line with recent research reporting that the ecoenzyme stoichiometry of different crop rhizospheres can vary greatly (Wang et al , 2022). The vector length (VL) of peanut rhizosphere soil was significantly shorter than that of maize rhizosphere, indicative of stronger nitrogen limitation, consistent with the typically higher nitrogen demand of legume crops. In particular, the combined treatment of intercropping and straw retention widened the vector angle (VA) by 5° in the maize rhizosphere, markedly alleviating nitrogen limitation there, which was directly related to the 11.41% increase in its TN content. The increase in VL by just 0.3 in the peanut rhizosphere reflected the intensification of carbon limitation there, which may be caused by greater competition for carbon sources among microorganisms due to better peanut growth under intercropping (Zhang et al, 2024 ). In comparison with global-scale research (Cui et al, 2023 ), the higher average VL value (1.61) in our study reveals the unique C-limitation characteristics of the black soil region in Northeast China. This could be related to the inhibition of carbon decomposition by low temperatures (Li et al, 2021 ), and also reflects the ecological adaptation mechanism of intercropping to overcome local carbon limitation by promoting root exudate inputs (Hu et al, 2021 ; Zhu et al, 2024 ). The correlation analysis and RDA (redundancy analysis) together indicated that the nutrient limitation status of rhizosphere microorganisms is regulated by multiple factors in concert. The carbon acquisition-activity of microorganisms in the maize rhizosphere is closely related to nitrogen and phosphorus availability, this suggesting that the improvement of nitrogen or phosphorus nutrients may affect the carbon cycling process by changing microbial metabolic strategies. By contrast, the peanut rhizosphere features a more complex regulatory pattern, in which microbial enzyme activities change synergistically with various soil nutrient indicators, reflecting the exceptional nutrient utilization facets of the legume rhizosphere (Feizi et al, 2023 ; Qiao et al, 2024 ). RDA further clarified the core role of microbial biomass nitrogen (MBN) in driving microbial nutrient limitation (cumulative explanation rate of 54.6%), indicating that microbial community structure directly shapes its nutrient acquisition strategy (Chen et al, 2025 ). Our study also found evidence showing that microorganisms can dynamically adjust extracellular enzyme secretion to adapt to local environmental changes. This plasticity is reflected not only in the differences in enzyme activities among different crop rhizospheres, but also in the changed stoichiometric ratios caused by the investigated management practices. Microbial nitrogen limitation in the rhizosphere will lead to the continuous degradation of soil functions, putting at risk the long-term productivity of farmland (Cui et al, 2025 ). Although increasing the application of nitrogen fertilizer may improve the crop yield and alleviate that microbial nitrogen limitation, to a certain extent, it may also lead to unintended negative effects, such as nitrogen leaching loss and higher production costs (Du et al , 2025). The results of the present study suggest that adjusting the cropping system (e.g., implementing a maize-peanut intercropping rotation) may be an effective way to rectify the imbalance between nitrogen fertilizer input and microbial nitrogen demand in food production, rather than simply adding more nitrogen. What makes the extracellular enzyme vector threshold so valuable is that it can provide an objective, quantitative indicator for nitrogen fertilizer management, but certain theoretical hurdles persist vis-à-vis the current extracellular enzyme stoichiometric threshold (Mori, 2024 ). Therefore, in future research, the nitrogen application range capable of balancing both crops and microorganisms should be determined empirically, through threshold identification, with a nitrogen application strategy that aims to balance the crop yield with soil microbial functioning proposed. In addition, we should note this study only focused on the maize-peanut intercropping system, so the regulatory effects of other crop type combinations require further testing and verification. Second, the association between microbial community composition (such as bacteria/fungi ratio) and extracellular enzyme activity was not analyzed in-depth here, so metagenomic sequencing should be incorporated in future similar work to further reveal the theoretical support for the postulated functional mechanisms operating. Conclusions Intercropping significantly increases the rhizospheric contents of soil organic carbon, total nitrogen, and total phosphorus, and promotes the accumulation of microbial biomass carbon, nitrogen, and phosphorus, with a significantly stronger response occurring in the peanut rhizosphere than in the maize rhizosphere. Straw retention mainly regulates the rhizosphere soil C:N:P stoichiometric balance, while intercropping significantly enhances extracellular enzyme activities, but these activities and corresponding stoichiometric characteristics clearly differ among between crop types. The combined practice of maize-peanut intercropping and straw retention is an effective agricultural technique for alleviating microbial nitrogen limitation in the rhizosphere and for advancing the sustainable development of farmland ecosystems in the black soil region of Northeast China. Declarations CRediT authorship contribution statement Qila Sa: Writing – original draft, Formal analysis, Data curation. Wei Qi: Writing – original draft, Formal analysis, Data curation. Fanyun Yao: Writing – review & editing, Funding acquisition. Jie Liang: Review & editing, Supervision. Yujun Cao: Review & editing. Yongjun Wang: Experimental design, Supervision, and Review. Acknowledgements This work was supported by the Basic Research Funds of JAAS (KYJF2025JJ003), and the Jilin Provincial Agricultural Science and Technology Innovation Project (CXGC2025RCY013). Declaration of Competing Interest The authors declare that they have no competing interests. References Ba X B, Sui X, Bao X l, et al. 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Zuccarini P, Asensio D, Ogaya R, et al. (2020). Effects of seasonal and decadal warming on soil enzymatic activity in a P-deficient Mediterranean shrubland. Global Change Biology . doi:10.1111/gcb.15077. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8696775","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594300437,"identity":"ff401275-0e2b-4ff0-bedf-117062460212","order_by":0,"name":"qila sa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYDACZuYGIGnDw8/fQLQWRpDSNBnJGQeItgas5bCNQUMCkRr4jjM2fvxRc57HgOEA44ePOURokTzM2CzNc+w2jzlzA7PkzG1EaDE4zNggzcB2m8ey4QAbMy+RWpp//vh3jsfgQALxWtokeNsOkKAF6Jc2a96+ZB7JGQebifML3/nDh2/++GZnz8/ffPDDR2K0MByAs8ARRJqWUTAKRsEoGAU4AABePjZyKoR+PwAAAABJRU5ErkJggg==","orcid":"","institution":"Jilin Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"qila","middleName":"","lastName":"sa","suffix":""},{"id":594300438,"identity":"c413a871-d0bb-4e1f-8769-38a7cf6a50a7","order_by":1,"name":"Wei Qi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Qi","suffix":""},{"id":594300439,"identity":"ea984bb6-16ab-48f8-a2f0-84a41920a641","order_by":2,"name":"Jie Liang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Liang","suffix":""},{"id":594300440,"identity":"fddd6d5a-f789-4289-b95f-9121993ddc88","order_by":3,"name":"YuJun Cao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"YuJun","middleName":"","lastName":"Cao","suffix":""},{"id":594300441,"identity":"cc85f217-24b6-4f69-b6c9-24c4a02b48db","order_by":4,"name":"FanYun Yao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"FanYun","middleName":"","lastName":"Yao","suffix":""},{"id":594300442,"identity":"79d4fdaf-721a-487e-a10d-1fa401f41470","order_by":5,"name":"YongJun Wang","email":"","orcid":"https://orcid.org/0000-0003-0780-184X","institution":"","correspondingAuthor":false,"prefix":"","firstName":"YongJun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-01-26 05:32:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8696775/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8696775/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103355795,"identity":"900cbdce-685a-4c5d-9a27-496200d07009","added_by":"auto","created_at":"2026-02-24 18:24:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32513,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots for the contents and stoichiometric ratios of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) in the rhizosphere of two crops under different treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: I0: Maize||peanut intercropping with root stubble retention; I100: Maize||peanut intercropping with full straw retention; S0: Sole cropping with root stubble retention; S100: Sole cropping with full straw retention. Lowercase letters above error bars indicate significant differences between treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), and uppercase letters indicate significant differences between crops under the same treatment (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The same below.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8696775/v1/76f57ddffd5f6160a7bf7b4f.png"},{"id":103355791,"identity":"33bd25d3-65c3-48b2-9ee8-211d1a742c49","added_by":"auto","created_at":"2026-02-24 18:24:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33577,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the effects of intercropping and residue retention on microbial biomass and stoichiometric ratios in rhizosphere soil of two crops.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8696775/v1/465b7f2cf8b25e36fc68fec5.png"},{"id":103355793,"identity":"67d83731-7815-40aa-a93a-ee76b75b28d1","added_by":"auto","created_at":"2026-02-24 18:24:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36628,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of the activities and stoichiometric ratios of extracellular carbon-acquiring enzyme (BG), nitrogen-acquiring enzymes (NAG, LAP), and phosphorus-acquiring enzyme (AP) in the rhizosphere soil of two crops under different treatments.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8696775/v1/f267adea6b87b8f33f9d1cf8.png"},{"id":103355800,"identity":"dfc149ee-3c6f-480d-bc50-274785c2bc03","added_by":"auto","created_at":"2026-02-24 18:24:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59978,"visible":true,"origin":"","legend":"\u003cp\u003eVector analysis of extracellular enzyme activities in rhizosphere soil of two crops under different treatments.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8696775/v1/f551edbe37d6727e5e93a29f.png"},{"id":103355792,"identity":"7c990b85-b2d3-4dec-9a86-841235849aba","added_by":"auto","created_at":"2026-02-24 18:24:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":330739,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of extracellular enzyme activities, microbial nutrient limitation, and rhizosphere soil properties in maize (a) and peanut crops (b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Red indicates a positive correlation, blue indicates a negative correlation. The width and color of the lines represent the strength of the correlation, and asterisks indicate the statistical significance level (* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e P\u003c/em\u003e\u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8696775/v1/f69a37665fa4a89aab2f2ee4.png"},{"id":103355797,"identity":"ba8060a7-1df6-4a62-b992-d02590930681","added_by":"auto","created_at":"2026-02-24 18:24:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100956,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis of soil extracellular enzymes, their stoichiometric ratios, and microbial biomass.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8696775/v1/5554ef9a7ee15777122727f2.png"},{"id":107329041,"identity":"58e3d8b4-37a8-4aa0-928e-8a03d7f93b2d","added_by":"auto","created_at":"2026-04-20 12:21:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1200482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8696775/v1/6fa5b27d-1fe5-4e28-8364-6548020750df.pdf"}],"financialInterests":"","formattedTitle":"Contrasting Rhizosphere Soil Stoichiometric Traits and Microbial Nitrogen Limitation between Maize and Peanut under Intercropping and Straw Retention","fulltext":[{"header":"Highlights","content":"\u003cul start=\"50\"\u003e\n \u003cli\u003eRhizosphere soil\u0026rsquo;s extracellular enzyme stoichiometric characteristics varied significantly between different crops\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMicrobial biomass nitrogen is a key factor affecting microbial nutrient limitation in rhizosphere soil\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIntercropping with straw retention alleviates nitrogen limitation, but intercropping has a stronger effect\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eIn China, the black soil region of its Northeast is a core grain production base, with irreplaceable strategic significance for national food security. In recent years, however, affected by factors such as soil erosion, long-term intensive cultivation, and poor nutrient management, this region has generally faced several concurrent problems, including thinning of the plow layer, decreased organic matter content, and soil function degradation (Wang et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In order to address and remedy this situation, it is imperative to explore more sustainable soil management models. As a proven and efficient agricultural management practice, intercropping has been widely applied around the world because of its advantages at improving soil quality and enhancing resource use efficiency (Ma et al, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, the maize-peanut intercropping system is capable of optimizing the composition and diversity of soil microbial community structure, which promotes soil health (Zhao et al, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, straw retention, another important practice in farming, can effectively enhance soil microbial activity by returning organic matter and nutrients to the soil, which promotes nutrient cycling and material transformation in agroecosystems (Wang et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), providing important support for ensuring high crop yields and sustainable farmland utilization.\u003c/p\u003e \u003cp\u003eSoil microorganisms, as core biological drivers of nutrient cycling, often have their metabolic activities limited by the local availability of certain soil nutrients (Cui et al, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Moorhead \u003cem\u003eet al\u003c/em\u003e, 2016). Accordingly, soil extracellular enzyme stoichiometry provides a fresh perspective for studying those kinds of microbial nutrient limitation. By analyzing the activities of key enzymes involved in microbial acquisition of carbon (β-1,4-glucosidase, BG ), nitrogen (β-1,4-N-acetylglucosaminidase, NAG; leucine aminopeptidase, LAP), and phosphorus (acid or alkaline phosphatase, AP), as well as their stoichiometric ratios (BG:(NAG\u0026thinsp;+\u0026thinsp;LAP) :AP), this method can accurately reflect the nutrient demands and utilization strategies of soil microbial communities (Liu et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sinsabaugh et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zuccarini et al, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many studies have shown that these enzyme activity ratios are closely related to soil C:N:P stoichiometric characteristics and can serve as effective indicators for assessing microbial nutrient limitation (Peng et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yang et al, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yao et al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rhizosphere, as the active interface zone for soil\u0026ndash;plant interactions, is far richer in microbial and extracellular enzyme activities than surrounding bulk soil (Gartner et al, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It is known that extracellular enzyme stoichiometric characteristics of the rhizosphere are affected not only by soil physical properties, but are also closely related to local vegetation types and land management practices (Cui et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Over the last few years, the benefits of straw retention and maize-legume intercropping for the rhizosphere microecosystem have been confirmed, in that both practices can substantially increase microbial diversity and soil nutrient availability (Jiang et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Song et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recently, we reported on how these two agricultural practices affect extracellular enzyme activities in bulk soil (Yao et al, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, critical aspects of microbial nutrient limitation in rhizosphere soil and their regulatory mechanisms under the combined management model of intercropping and straw retention remain unclear, which precludes a comprehensive understanding of their ecological effects.\u003c/p\u003e \u003cp\u003eUsing the enzyme eco-stoichiometry approach, this study focused on exploring the effects of maize-peanut intercropping and straw retention on microbial nutrient limitation in rhizosphere soil. We address three pertinent scientific questions: (1) How do rhizosphere soil properties, microbial biomass, extracellular enzyme activities, and stoichiometric ratios of maize and peanut respond to the combined practices of intercropping and straw retention? (2) What is the status of rhizosphere soil microbial nutrient limitation of maize and peanut under those different practices? (3) What are the main factors driving rhizosphere soil\u0026rsquo;s nutrient limitation of maize and peanut crops? Answering these timely questions can provide a theoretical basis for the sustainable management of farmland ecosystems in China\u0026rsquo;s vital black soil region.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSite description\u003c/h2\u003e \u003cp\u003eThis study was conducted at the Halahai Comprehensive Experimental Station of Jilin Academy of Agricultural Sciences (44\u0026deg;05\u0026prime;N, 124\u0026deg;51\u0026prime;E), where a long-term, fixed-field experiment platform for maize-peanut intercropping (with rotation in the following year) was established in 2015. The study area has a temperate continental monsoon climate, with an average annual temperature of 4.7\u0026deg;C and an average annual precipitation of 507.7 mm. The tested soil is chernozem with a loamy texture. The 0\u0026ndash;20 cm plow layer has these soil fertility properties: alkaline hydrolyzable nitrogen: 47.37 mg/kg, available phosphorus: 13.27 mg/kg, available potassium: 173.68 mg/kg, organic matter: 13.79 g/kg, and pH\u0026thinsp;=\u0026thinsp;7.86.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental design\u003c/h3\u003e\n\u003cp\u003eThis study used split-plot design with blocking. In this hierarchical design (two levels), the main plot stratum corresponded to three planting patterns: maize||peanut 6:6 equal-width intercropping (row ratio of 1:1), peanut sole cropping, and maize sole cropping; the subplot stratum consisted of two treatments: full straw retention (100%) and root stubble retention (only root stubble retained). Four treatment combinations were selected for analysis: maize||peanut with root stubble retention (I0), maize||peanut with full straw retention (I100), sole cropping with root stubble retention (S0, control), and sole cropping with full straw retention (S100). A total of 9 main plots were set (=\u0026thinsp;3 replicates\u0026times;3 planting patterns), each intercropping treatment was set as 120 m \u0026times; 3.9 m, and each sole cropping treatment was set as 120 m \u0026times; 10.4 m. The two tested crops were the semi-compact maize variety \u0026lsquo;Fumin 985\u0026rsquo; and the early-maturing upright-flowering peanut variety \u0026lsquo;Huayu 20\u0026rsquo;, with a unified row spacing of 0.65 m. Maize was sown on 7 May 2024, at a planting density of 60 000 plants\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. Compound fertilizer (N:P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e:K\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;=\u0026thinsp;15:15:15) was applied as base fertilizer at a rate of 970 kg\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and urea (N content: 46%) was top-dressed at the large bell stage at a rate of 163 kg\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Peanut was sown on 13 May 2024, at a planting density of 120 000 holes\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e(2 seeds per hole). Compound fertilizer (N:P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e:K\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;=\u0026thinsp;12:17:16) was applied as base fertilizer at a rate of 700 kg\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with no top-dressing during the growth period. Straw treatment was conducted after maize harvest in early October: for the root stubble retention treatment, the aboveground straw of maize was completely removed; for the full straw retention treatment, the straw was crushed to about 10 cm and plowed into the soil.\u003c/p\u003e\n\u003ch3\u003eSoil sample collection\u003c/h3\u003e\n\u003cp\u003eSoil samples were collected during the milk-ripe stage of maize and the pod-filling stage of peanut. To obtain rhizosphere soil, we used the soil-shaking method and followed these steps: from each plot, three maize and six peanut plants were selected as representative individuals, with a 30-cm \u0026times; 30-cm \u0026times; 30-cm root zone soil block excavated per crop type. After carefully removing any surface debris, the loose soil was removed by gentle shaking, and the soil adhering to within 5 mm of the surface of fine roots (diameter\u0026thinsp;\u0026lt;\u0026thinsp;2 mm) was collected as the rhizosphere samples. These samples were then fully mixed and passed through a 2-mm sieve to remove any residual roots and other impurities. Next, each composite sample was immediately divided into two parts for processing. One part was quickly frozen with liquid nitrogen and stored in an ultra-low temperature refrigerator at \u0026minus;\u0026thinsp;80\u0026deg;C for the determination of microbial biomass carbon (MBC), biomass nitrogen (MBN), biomass phosphorus (MBP), and extracellular enzyme activities related to C, N, and P cycling. The other part was naturally air-dried for the analysis of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents.\u003c/p\u003e\n\u003ch3\u003eSoil sample measurements\u003c/h3\u003e\n\u003cp\u003eStandard methods were used to determine the basic physical and chemical properties of soil. The SOC content was quantified with a total organic carbon analyzer. The TN content was measured via the Kjeldahl method, with these specific steps: soil samples were digested with concentrated H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e until they appeared clear, then volumetrically fixed, and distilled and titrated using a Kjeldahl nitrogen analyzer (Bao, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bremner, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The TP content was quantified by the H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e-HClO\u003csub\u003e4\u003c/sub\u003e digestion-molybdenum antimony anti-colorimetry method: after complete digestion, the volume was fixed, the supernatant was taken, molybdenum antimony anti-color developer was added, and colorimetry performed at a 700-nm wavelength (Murphy \u003cem\u003eet al\u003c/em\u003e, 1962).\u003c/p\u003e \u003cp\u003eMicrobial biomass was determined using the chloroform fumigation-extraction method (Margesin \u003cem\u003eet al\u003c/em\u003e, 2005). During chloroform fumigation, to kill living soil microorganisms as completely as possible, soil samples were placed in Petri dishes, put into a desiccator, and the fumigation time in chloroform vapor extended from 24 h to 48 h. For the MBN content\u0026rsquo;s determination, after extraction with 0.5 mol\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e solution, the carbon-nitrogen ratio content was determined by high-temperature oxidation in a total organic carbon analyzer. When calculating the MBC:MBN content, the conversion coefficients used for the interpolation of C, N, and P contents between the fumigated and non-fumigated soils were 0.45, 0.54, and 0.40, respectively (Mueller. 1996).\u003c/p\u003e \u003cp\u003eThe respective activity of four extracellular enzymes related to C, N, and P cycling, namely β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG) and leucine aminopeptidase (LAP), and alkaline phosphatase (AP), were determined using the microplate fluorometric method (German et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). All sample preparations for these extracellular enzyme activity determinations were completed in advance. Once prepared, the soil previously stored frozen (at \u0026minus;\u0026thinsp;80\u0026deg;C) was transferred to a 4\u0026deg;C environment for thawing one day prior. Taking six soil samples as a group, 1.5 g of soil was accurately weighed and placed in a hard glass bottle, to which 150 mL of buffer solution was added, and the mixture then fully stirred for 2 min (using a stirrer). Next, the soil mixture was transferred to a small bowl through a filter, poured into a rotor, and the reaction solution slowly added to it under continuous stirring (with a magnetic stirrer). Finally, enzyme activity was recorded using a multi-functional microplate reader. These determinations were made for three replicates per enzyme, with enzyme activity expressed as the amount of product produced per unit mass of soil per unit time.\u003c/p\u003e\n\u003ch3\u003eMicrobial nutrient limitation\u003c/h3\u003e\n\u003cp\u003eBased on the stoichiometric characteristics of the above extracellular enzyme activities, a vector analysis model was applied to evaluate the nutrient limitation status of soil microorganisms (Moorhead \u003cem\u003eet al\u003c/em\u003e. 2023; Sinsabaugh \u003cem\u003eet al,\u0026nbsp;\u003c/em\u003e2012). First, the respective activity values of BG, NAG, LAP, and AP were transformed to their natural logarithm (ln), and enzyme activity ratios then calculated this way:\u003c/p\u003e\n\u003cp\u003eEnzyme C:N=ln（BG）/ln（NAG+LAP）........................ （1）\u003c/p\u003e\n\u003cp\u003eEnzyme C:P=ln（BG）/ln（AP）...................................... （2）\u003c/p\u003e\n\u003cp\u003eEnzyme N:P=ln（NAG+LAP）/ln（AP）......................... （3）\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, enzyme C:N refers to the soil enzyme carbon-to-nitrogen ratio; enzyme C:P refers to the carbon-to-phosphorus ratio; and enzyme N:P refers to the nitrogen-to-phosphorus ratio. Vector analysis was used to quantify the degree of microbial nutrient limitation, as follows:\u003c/p\u003e\n\u003cp\u003eVL = SQRT（X\u003csup\u003e2\u003c/sup\u003e +Y\u003csup\u003e2\u003c/sup\u003e）....................................................... （4）\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVA = Degrees [ATAN2（X,Y）]......................................... （5）\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the above two equations, X = ln (NAG + LAP) / ln (AP) and Y = ln (BG) / ln (NAG + LAP); SQRT denotes the square root function; Degrees is the function for converting radians to degrees; and ATAN2 is the arctangent function. A longer vector length (VL) indicates a greater degree of C limitation on soil microorganisms; a vector angle (VA) \u0026gt; 45°\u0026nbsp;means microorganisms are limited by P, while a VA \u0026lt; 45°\u0026nbsp;indicates N limitation (Moorhead \u003cem\u003eet al\u003c/em\u003e, 2015). Recent studies have suggested using optimal theoretical thresholds (0.61°\u0026nbsp;and 55°) as robust criteria for distinguishing microbial N and P limitations (Cui\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll empirical data were initially organized using MS Excel 2021, with their statistical analysis performed in SPSS 18.0 software. Three-way ANOVAs (analysis of variance) were used to test the effects of crop species, treatment methods, and their interactions on rhizosphere soil nutrient characteristics, microbial biomass (C, N, P), and extracellular enzyme activity indicators. Redundancy analysis (RDA) and Pearson correlations were used to reveal the relationships among various indicators. Vector analysis diagrams and heatmaps were drawn in Origin Pro 2022 software, and the data presented as the mean ± standard error (n = 3).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll empirical data were initially organized using MS Excel 2021, with their statistical analysis performed in SPSS 18.0 software. Three-way ANOVAs (analysis of variance) were used to test the effects of crop species, treatment methods, and their interactions on rhizosphere soil nutrient characteristics, microbial biomass (C, N, P), and extracellular enzyme activity indicators. Redundancy analysis (RDA) and Pearson correlations were used to reveal the relationships among various indicators. Vector analysis diagrams and heatmaps were drawn in Origin Pro 2022 software, and the data presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRhizosphere soil\u0026rsquo;s physicochemical properties and nutrient stoichiometry\u003c/h2\u003e \u003cp\u003eAs Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows, the planting pattern (intercropping vs. sole cropping) significantly affected the rhizosphere contents of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Specifically, compared with both sole cropping treatments (S0 and S100), both intercropping treatments (I0 and I100) increased the SOC content by 6.21% and 13.57%, the TN content by 8.57% and 12.49%, and the TP content by 12.01% and 40.29% in the maize and peanut rhizospheres, respectively. Notably, combining the intercropping and full straw retention (I100) practices had a more significant promoting effect: it increased the TN content in the maize rhizosphere by 11.41% relative to the control (S0), and increased the TP content in the maize and peanut rhizospheres by 22.81% and 43.41%, respectively (Fig.\u0026nbsp;1). However, the straw retention practice (root stubble retention/full straw retention) did not significantly affect the SOC, TN, or TP content, but did change the SOC:TN ratio significantly. Also, the rhizospheric TN content differed significantly between the two crops (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), being generally lower in the peanut than maize rhizosphere. Stoichiometric analysis showed that both intercropping and straw retention significantly lowered the TN:TP and SOC:TP ratios, with a significant interaction effect of crop species and planting pattern on the either ratio (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of ANOVAs assessing the main effects and interactions for planting pattern (PP), residue retention (RR), and Crop species (CS) on rhizosphere soil SOC, TN, TP contents and their stoichiometric ratios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOC:TN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOC:TP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTN:TP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9.77**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e20.452**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e27.123***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.110n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e17.890**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e19.389**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.019n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.186n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.444n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.164*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e12.516**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4.818*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.757**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.163n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.044n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.587n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.730n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.227n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.416n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.231*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.644n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.302*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e10.541*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.419n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.820n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.526n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.015n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.449n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.900n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.342n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.921n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;PP\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.297n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.186n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.127n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.688n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.431n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Bold type indicates \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, while *, **, and *** corresponding to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively. n.s. indicates no significant effect at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level. The values are \u003cem\u003eF\u003c/em\u003e-test statistic. The same below.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial biomass and stoichiometry of rhizosphere soil\u003c/h2\u003e \u003cp\u003ePlanting pattern and crop species had significant effects on the microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) contents of rhizosphere soil (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Intercropping significantly increased MBC in the peanut rhizosphere (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with both sole cropping treatments (S0 and S100), intercropping increased MBC by 0.20% and 35.72%, MBN by 13.43% and 38.11%, and MBP by 14.06% and 50.94% in the maize and peanut rhizospheres, respectively. This indicated that intercropping\u0026rsquo;s positive impact on microbial biomass in the peanut rhizosphere surpassed that in the maize rhizosphere (Fig.\u0026nbsp;2). Stoichiometric analysis of microbial biomass showed that, in the maize rhizosphere, the MBC:MBN, MBC:MBP, and MBN:MBP ratios were 8.01\u0026thinsp;~\u0026thinsp;9.82, 16.92\u0026thinsp;~\u0026thinsp;23.84, and 2.12\u0026thinsp;~\u0026thinsp;2.66, respectively, while in the peanut rhizosphere the corresponding ratios were 10.96\u0026thinsp;~\u0026thinsp;15.29, 19.38\u0026thinsp;~\u0026thinsp;23.10, and 1.39\u0026thinsp;~\u0026thinsp;1.93. The MBC:MBN ratio was significantly affected by crop species, the interaction between planting pattern and straw retention method (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as the three-way interaction among those factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the MBN:MBP ratio was affected chiefly by crop species (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); while the MBC:MBP ratio was similar among different treatments. When compared with the control (S0) treatment, the combination of intercropping and full straw retention (I100) decreased the MBC:MBN ratio by 22.64% in maize rhizosphere soil, but increased it by 5.20% in the peanut rhizosphere. This further confirmed the divergent responses of microbial communities in different crop rhizospheres to management practices.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-way ANOVAs testing the effects of different treatments on rhizosphere soil contents of MBC, MBN, MBP and their stoichiometric ratios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMBN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMBC: MBN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMBC: MBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMBN: MBP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.843*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10.261**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e14.058**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.936n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.656n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.898n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.986n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.719n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.876n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11.055**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e85.100***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.437**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e40.211***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e14.2963**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.688*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.314n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.543n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.620n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.153n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.103n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.354n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.919n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.179n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.679n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.190n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.368n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.447n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9.148**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.350n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.529n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;PP\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.103n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.403n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.230n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.190*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.411n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.767n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExtracellular enzyme activities of rhizosphere soil and their stoichiometric characteristics\u003c/h2\u003e \u003cp\u003eAs Fig.\u0026nbsp;3 shows, intercropping significantly bolstered the extracellular enzyme activities in rhizosphere soil (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The activity range of the carbon-acquiring enzyme (BG), nitrogen-acquiring enzymes (NAG\u0026thinsp;+\u0026thinsp;LAP), and phosphorus-acquiring enzyme (AP) in the maize rhizosphere were 22.10\u0026thinsp;~\u0026thinsp;44.62, 25.65\u0026thinsp;~\u0026thinsp;34.23, and 11.77\u0026thinsp;~\u0026thinsp;16.68 \u0026micro;mol\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, while the corresponding enzyme activities in peanut rhizosphere were 15.36\u0026thinsp;~\u0026thinsp;28.81, 25.39\u0026thinsp;~\u0026thinsp;36.06, and 8.60\u0026thinsp;~\u0026thinsp;14.20 \u0026micro;mol\u0026middot;g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Compared with sole cropping (S0 and S100), intercropping (I0 and I100) increased BG activity by 63.68% and 64.99%, AP activity by 34.53% and 49.51%, and NAG\u0026thinsp;+\u0026thinsp;LAP activity by 18.44% and 29.62% in the maize and peanut rhizospheres, respectively. As well, BG and AP activities were significantly lower in the peanut than maize rhizosphere (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, applying intercropping and straw retention in combination (I100) enhanced enzyme activities more so than using either treatment alone, increasing BG activity by 94.23% and 70.34% in the maize and peanut rhizospheres, respectively. We detected significant differences in the C, N, P stoichiometric ratios of extracellular enzymes in the rhizosphere between the two crop types (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Intercropping augmented the BG:(NAG\u0026thinsp;+\u0026thinsp;LAP) ratio by 36.52% and 27.22%, and the BG:AP ratio by 21.36% and 11.59%, in the maize and peanut rhizospheres, respectively. Both BG activity and the BG:AP ratio were significantly affected by the combined practice of intercropping and straw retention (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while straw retention method alone had no discernible effect on enzyme activities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-way ANOVAs testing the effects of different treatments on BG, NAG\u0026thinsp;+\u0026thinsp;LAP, AP activities and their stoichiometric ratios in rhizosphere soil.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAG\u0026thinsp;+\u0026thinsp;LAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBG:(NAG\u0026thinsp;+\u0026thinsp;LAP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(NAG\u0026thinsp;+\u0026thinsp;LAP):AP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBG:AP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003cp\u003eRR\u003c/p\u003e \u003cp\u003eCS\u003c/p\u003e \u003cp\u003eCS\u0026times;PP\u003c/p\u003e \u003cp\u003eCS\u0026times;RR\u003c/p\u003e \u003cp\u003ePP\u0026times;RR\u003c/p\u003e \u003cp\u003eCS\u0026times;PP\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e53.967***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.992n.s.\u003c/p\u003e \u003cp\u003e\u003cb\u003e48.336***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e2.676n.s.\u003c/p\u003e \u003cp\u003e1.015n.s.\u003c/p\u003e \u003cp\u003e\u003cb\u003e4.569*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.570n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e15.934**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1.420n.s.\u003c/p\u003e \u003cp\u003e0.n.s.\u003c/p\u003e \u003cp\u003e0.654n.s.\u003c/p\u003e \u003cp\u003e1.010n.s.\u003c/p\u003e \u003cp\u003e1.613n.s.\u003c/p\u003e \u003cp\u003e0.126n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e33.078***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.001n.s.\u003c/p\u003e \u003cp\u003e\u003cb\u003e20.203**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.008n.s.\u003c/p\u003e \u003cp\u003e0.881n.s.\u003c/p\u003e \u003cp\u003e1.107n.s.\u003c/p\u003e \u003cp\u003e0.334n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e29.354***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.161n.s.\u003c/p\u003e \u003cp\u003e\u003cb\u003e81.120***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e3.606n.s.\u003c/p\u003e \u003cp\u003e0.088n.s.\u003c/p\u003e \u003cp\u003e3.606n.s.\u003c/p\u003e \u003cp\u003e1.894n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.248n.s.\u003c/p\u003e \u003cp\u003e0.952n.s.\u003c/p\u003e \u003cp\u003e\u003cb\u003e18.711**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.030ns.\u003c/p\u003e \u003cp\u003e0.002n.s.\u003c/p\u003e \u003cp\u003e0.060n.s.\u003c/p\u003e \u003cp\u003e0.006n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e13.751**\u003c/b\u003e\u003c/p\u003e \u003cp\u003e3.539n.s.\u003c/p\u003e \u003cp\u003e\u003cb\u003e20.450***\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1.792n.s.\u003c/p\u003e \u003cp\u003e0.144n.s.\u003c/p\u003e \u003cp\u003e\u003cb\u003e4.712*\u003c/b\u003e\u003c/p\u003e \u003cp\u003e2.254n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eVector analysis of extracellular enzyme activities in rhizosphere soil\u003c/h2\u003e \u003cp\u003eThe vector analysis model uncovered significant differences in the stoichiometric characteristics of extracellular enzymes under the different treatments and crop types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). In both maize and peanut rhizosphere soils, the vector length (VL) of each extracellular enzyme was greater than 1.46, with vector angles (VA) smaller than 45\u0026deg;, indicating that soil microorganisms in the study area were simultaneously C- and N-limited. The I100 treatment had the largest VL value, indicating the highest degree of carbon limitation, and its VA value was also the largest, implying significantly alleviated nitrogen limitation; in contrast, the S100 treatment had a smaller VL value, with relatively weaker carbon limitation. ANOVA results showed that planting pattern and crop species had significant main effects on microbial metabolic characteristics (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), along with a significant interaction effect of planting pattern and residue retention on VL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, compared with either sole cropping treatment (S0 and S100), intercropping (I0 and I100) significantly increased the VL by 2.10% in the maize rhizosphere. Further analysis of the ln(BG):ln(NAG\u0026thinsp;+\u0026thinsp;LAP) and ln(BG):ln(AP) ratios confirmed that all treatments were in the realm of carbon and nitrogen limitations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003ec); however, the degree of nitrogen limitation faced by microorganisms in the peanut rhizosphere (average VA\u0026thinsp;=\u0026thinsp;34.67\u0026deg;) was significantly stronger than that in the maize rhizosphere (average VA\u0026thinsp;=\u0026thinsp;37.80\u0026deg;), which was highly consistent with the earlier vector analysis results. There was a significant positive correlation between VL and VA in both maize and peanut rhizospheres (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), which may indicate that carbon input practices such as straw retention, while alleviating carbon limitation (shorter VL), nonetheless intensify microbial competition for nitrogen (smaller VA means stronger nitrogen limitation). Therefore, in actual field production settings, nitrogen application usually needs to be supplemented simultaneously after straw retention to balance carbon and nitrogen.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-way ANOVAs testing the effects of different treatments on the vector length (VL) and vector angle (VA) of extracellular enzyme activities and extracellular enzymatic stoichiometric ratios in rhizosphere soil.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14.038**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.338**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.683n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.346n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e37.486***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e23.554***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;PP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.203n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.266n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.645*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u0026times;PP\u0026times;RR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.879n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation analysis between soil nutrients and microbial biomass, and the C:N:P stoichiometric ratios of extracellular enzymes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Pearson correlations revealed close relationships between soil properties and microbial metabolic characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the maize rhizosphere, soil BG activity showed significant or highly significant positive correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with TN, TP, and MBCMBN. The activity of NAG\u0026thinsp;+\u0026thinsp;LAP was significantly positively correlated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with TN as well as MBN. The AP activity had strong, significant positive correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with TN, TP, and MBC:MBN. Regarding VL, its correlation with TP and TN:TP was positive and significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn the peanut rhizosphere, more significant correlations were observed: the BG, NAG\u0026thinsp;+\u0026thinsp;LAP, and AP activities all had highly significant positive correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with SOC, TN, TP, MBC, MBP, and TN:TP, SOC:TP ratios, and a significant positive correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with MBN:MBP. The activity of AP had as significant positive correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with MBN. Finally, VL showed a significant positive correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with SOC:TP, while VA had highly significant positive correlations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with SOC, TN, TP, and MBP, and a significant positive correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with MBC.\u003c/p\u003e \u003cp\u003eTo assess the effects of SOC, TN, TP, microbial biomass C, N, P, and their stoichiometric ratios on microbial nutrient limitation in rhizosphere soil, we used RDA to examine relationships between relevant indicators and VL, VA under the contrasting planting patterns and straw retention methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These results showed that soil physicochemical properties, microbial biomass, and their stoichiometric characteristics together explained 79.51% of the variation in microbial nutrient limitation; among them, three factors\u0026mdash;MBN (54.6%), MBC:MBN (5.1%), and TN (8.6%)\u0026mdash;best explained the changes in VL and VA in rhizosphere soil. Both VL and VA were distributed in the same direction as rhizosphere soil SOC, TN, TP, MBC, MBN, MBP, and MBN:MBP, showing positive correlations with small angles and long arrows; conversely, both featured negative correlations with SOC:TN, SOC:TP, TN:TP, MBC:MBP, and MBC:MBN. This confirms the core role of microbial biomass and its stoichiometric characteristics in influencing nutrient limitation in rhizosphere soils of crops in the study area, especially the high explanatory power of MBN.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eEffects of intercropping and straw retention on rhizosphere soil nutrients, microbial biomass, and their stoichiometric characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIntercropping and straw retention are key agricultural practices that can help to regulate rhizosphere soil nutrients and stoichiometric characteristics. The results of this study show that the maize-peanut intercropping system significantly improved the rhizosphere soil\u0026rsquo;s nutrient status via multiple mechanisms. Intercropping not only increased the amount of organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) in rhizosphere soil, but also, and more importantly, it achieved a synergistic improvement of nutrients by promoting the proliferation of nitrogen-fixing and phosphorus-solubilizing bacteria in the rhizosphere (Zan et al, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), increasing root exudates and residue inputs (Zhao et al, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and optimizing soil microbial community structure (Zhu et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma et al, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, we find that straw retention mainly increased the carbon input and intensified microbial competition for nitrogen and phosphorus, thereby disrupting the original soil C:N:P balance and leading to significant changes in the soil carbon, nitrogen, and phosphorus ratios (Li et al, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This pronounced discrepancy highlights the distinct regulatory mechanisms of different agricultural practices upon soil nutrient cycling dynamics. Our field experimental results confirm that soil ecological stoichiometric characteristics can serve as sensitive indicators for assessing the effects of farmland management measures (Yang et al, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMaize-peanut intercropping significantly increased the contents of microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) in rhizosphere soil, but there were marked differences how they responded under the two crop types. We found a stronger response of microbial biomass to intercropping in the peanut rhizosphere than in the maize rhizosphere, a result closely tied to the unique root nodule nitrogen-fixing system of legumes and the microbial microenvironment shaped by specific root exudates (Li et al, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, in the present study, the average MBC:MBN ratio in all treatments was above 7, this indicating that fungi dominated the microbial community in the study area (Hessen et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Yet the combined treatment of intercropping and straw retention decreased the MBC:MBN ratio in maize rhizosphere, indicating a shift in microbial community towards bacterial dominance (Guan et al, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, the lower MBN:MBP ratio in the peanut rhizosphere implies that nitrogen limitation is stronger there (Cui et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liu et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), providing new evidence for better understanding the differences in rhizosphere microecology between legume and non-legume crops. These results deepen our knowledge of pivotal aspects of microbial nutrient limitation in the rhizosphere of different crops and offer a theoretical basis for precision agricultural management.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects of intercropping and straw retention on extracellular enzyme activities in rhizosphere soil and their stoichiometric characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study found that maize-peanut intercropping significantly enhanced the activities of a carbon-acquiring enzyme (BG), nitrogen-acquiring enzymes (NAG\u0026thinsp;+\u0026thinsp;LAP), and a phosphorus-acquiring enzyme (AP), with nutrient-specific mechanisms. That is, intercropping increased the input of soil organic matter into the rhizosphere, likely providing sufficient carbon sources for microorganisms to bolster the activity of BG (Wang \u003cem\u003eet al\u003c/em\u003e, 2022); it also improved nitrogen availability and optimized microbial community structure, driving the organic N-mineralization process mediated by NAG\u0026thinsp;+\u0026thinsp;LAP (Ba et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, the soil TP content also increased under intercropping, with AP activity being notably strengthened (Qu et al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We also found that the BG and NAG\u0026thinsp;+\u0026thinsp;LAP activities in maize rhizosphere soil exceeded those in peanut rhizosphere soil, but vice versa for the activity of acid phosphatase (AP), suggesting crucial differences exist in the extracellular enzyme stoichiometric characteristics of different crops\u0026rsquo; rhizosphere soils. In this study, the extracellular enzyme C:N:P ratios in maize and peanut rhizosphere soils were 1.33:1.29:1.00 and 0.89:1.29:1.00, respectively, this deviating noticeably from the global average soil enzyme C:N:P ratio of approximately 1:1:1 (L et al, 2008). This discrepancy may be attributable to the high organic matter content in the black soil of the maize-growing area, where microorganisms must bolster their carbon-decomposing enzymes to meet their metabolic needs. On the other hand, the BG:(NAG\u0026thinsp;+\u0026thinsp;LAP) ratio in the peanut area likely lined to the low BG activity and a carbon-nitrogen coupling effect caused by nitrogen fixation by that legume plant. This finding provides new evidence for understanding the adaptive differentiation of nutrient acquisition strategies in rhizosphere microorganisms of different crops (Ma et al, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects of intercropping and straw retention on the characteristics of microbial resource limitations in rhizosphere soil\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMicrobial resource limitations constitute a fundamental link connecting soil, microorganisms, and plant crops, whose status at given place and time directly determines the nutrient use efficiency, soil health, and sustainability of farmland (Sinsabaugh et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Using the vector analysis model (Moorhead \u003cem\u003eet al\u003c/em\u003e, 2016; Cui et al, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our study revealed that microorganisms in both maize and peanut rhizospheres are limited by both carbon and nitrogen, but crop-specific differences were evident. This finding is in line with recent research reporting that the ecoenzyme stoichiometry of different crop rhizospheres can vary greatly (Wang \u003cem\u003eet al\u003c/em\u003e, 2022).\u003c/p\u003e \u003cp\u003eThe vector length (VL) of peanut rhizosphere soil was significantly shorter than that of maize rhizosphere, indicative of stronger nitrogen limitation, consistent with the typically higher nitrogen demand of legume crops. In particular, the combined treatment of intercropping and straw retention widened the vector angle (VA) by 5\u0026deg; in the maize rhizosphere, markedly alleviating nitrogen limitation there, which was directly related to the 11.41% increase in its TN content. The increase in VL by just 0.3 in the peanut rhizosphere reflected the intensification of carbon limitation there, which may be caused by greater competition for carbon sources among microorganisms due to better peanut growth under intercropping (Zhang et al, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In comparison with global-scale research (Cui et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the higher average VL value (1.61) in our study reveals the unique C-limitation characteristics of the black soil region in Northeast China. This could be related to the inhibition of carbon decomposition by low temperatures (Li et al, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and also reflects the ecological adaptation mechanism of intercropping to overcome local carbon limitation by promoting root exudate inputs (Hu et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe correlation analysis and RDA (redundancy analysis) together indicated that the nutrient limitation status of rhizosphere microorganisms is regulated by multiple factors in concert. The carbon acquisition-activity of microorganisms in the maize rhizosphere is closely related to nitrogen and phosphorus availability, this suggesting that the improvement of nitrogen or phosphorus nutrients may affect the carbon cycling process by changing microbial metabolic strategies. By contrast, the peanut rhizosphere features a more complex regulatory pattern, in which microbial enzyme activities change synergistically with various soil nutrient indicators, reflecting the exceptional nutrient utilization facets of the legume rhizosphere (Feizi et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qiao et al, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). RDA further clarified the core role of microbial biomass nitrogen (MBN) in driving microbial nutrient limitation (cumulative explanation rate of 54.6%), indicating that microbial community structure directly shapes its nutrient acquisition strategy (Chen et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Our study also found evidence showing that microorganisms can dynamically adjust extracellular enzyme secretion to adapt to local environmental changes. This plasticity is reflected not only in the differences in enzyme activities among different crop rhizospheres, but also in the changed stoichiometric ratios caused by the investigated management practices.\u003c/p\u003e \u003cp\u003eMicrobial nitrogen limitation in the rhizosphere will lead to the continuous degradation of soil functions, putting at risk the long-term productivity of farmland (Cui et al, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although increasing the application of nitrogen fertilizer may improve the crop yield and alleviate that microbial nitrogen limitation, to a certain extent, it may also lead to unintended negative effects, such as nitrogen leaching loss and higher production costs (Du \u003cem\u003eet al\u003c/em\u003e, 2025). The results of the present study suggest that adjusting the cropping system (e.g., implementing a maize-peanut intercropping rotation) may be an effective way to rectify the imbalance between nitrogen fertilizer input and microbial nitrogen demand in food production, rather than simply adding more nitrogen. What makes the extracellular enzyme vector threshold so valuable is that it can provide an objective, quantitative indicator for nitrogen fertilizer management, but certain theoretical hurdles persist vis-\u0026agrave;-vis the current extracellular enzyme stoichiometric threshold (Mori, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, in future research, the nitrogen application range capable of balancing both crops and microorganisms should be determined empirically, through threshold identification, with a nitrogen application strategy that aims to balance the crop yield with soil microbial functioning proposed. In addition, we should note this study only focused on the maize-peanut intercropping system, so the regulatory effects of other crop type combinations require further testing and verification. Second, the association between microbial community composition (such as bacteria/fungi ratio) and extracellular enzyme activity was not analyzed in-depth here, so metagenomic sequencing should be incorporated in future similar work to further reveal the theoretical support for the postulated functional mechanisms operating.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIntercropping significantly increases the rhizospheric contents of soil organic carbon, total nitrogen, and total phosphorus, and promotes the accumulation of microbial biomass carbon, nitrogen, and phosphorus, with a significantly stronger response occurring in the peanut rhizosphere than in the maize rhizosphere. Straw retention mainly regulates the rhizosphere soil C:N:P stoichiometric balance, while intercropping significantly enhances extracellular enzyme activities, but these activities and corresponding stoichiometric characteristics clearly differ among between crop types. The combined practice of maize-peanut intercropping and straw retention is an effective agricultural technique for alleviating microbial nitrogen limitation in the rhizosphere and for advancing the sustainable development of farmland ecosystems in the black soil region of Northeast China.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQila Sa: Writing – original draft, Formal analysis, Data curation. Wei Qi: Writing – original draft, Formal analysis, Data curation. Fanyun Yao: Writing – review \u0026amp; editing, Funding acquisition. Jie Liang: Review \u0026amp; editing, Supervision. Yujun Cao: Review \u0026amp; editing. Yongjun Wang: Experimental design, Supervision, and Review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Basic Research Funds of JAAS (KYJF2025JJ003), and the Jilin Provincial Agricultural Science and Technology Innovation Project (CXGC2025RCY013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBa X B, Sui X, Bao X l, et al. (2022). Impacts of Intercropping with Cover Crops andMaize on Soil Carbon and Nitrogen Contents and Related Enzyme Activities. \u003cem\u003eChinese Journal of Soil Science\u003c/em\u003e. \u003cstrong\u003e53\u003c/strong\u003e(3):577\u0026minus;587.doi:10.19336/j.cnki.trtb.2021122601.\u003c/li\u003e\n\u003cli\u003eBao, S. (2000). 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Effects of seasonal and decadal warming on soil enzymatic activity in a P-deficient Mediterranean shrubland. \u003cem\u003eGlobal Change Biology\u003c/em\u003e. doi:10.1111/gcb.15077.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"C:N:P stoichiometry, Extracellular enzyme activity, Microbial biomass, Microbial nutrient limitation, Soil nutrients","lastPublishedDoi":"10.21203/rs.3.rs-8696775/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8696775/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAims\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtracellular enzyme stoichiometry is a key indicator for assessing resource limitations faced by soil microorganisms. Yet the characteristics of microbial resource limitation in rhizosphere soil under the combined agricultural practices of intercropping and straw retention remain unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHere, we conducted a field experiment in the black soil region of Northeast China, to quantify the effects of intercropping and straw retention on soil nutrients, microbial biomass, extracellular enzyme activities, and their C:N:P stoichiometry in the rhizosphere of maize and peanut crops.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur results revealed an average vector length (VL) of 1.68 and 1.57 for extracellular enzymes in the rhizosphere soil of maize and peanut, with a vector angle (VA) of 37.80° and 34.67°, respectively. This indicated that soil microorganisms in the rhizosphere of both crops were co-limited by C and N, and the N limitation was more significant in the peanut rhizosphere. Notably, the combined treatment of intercropping and full straw retention increased the VA by 5°, effectively alleviating N limitation in the rhizosphere soil. The extracellular enzyme C:N:P stoichiometry in the rhizosphere soil of maize and peanut was 1.33:1.29:1.00 and 0.89:1.29:1.00, respectively. Microbial biomass nitrogen (MBN) was the primary factor affecting microbial nutrient limitation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe extracellular enzyme stoichiometric characteristics of rhizosphere soil differed significantly between the two crops. Intercropping had a stronger impact on rhizosphere microbial nutrient limitation than straw retention, and their synergistic effect could significantly alleviate rhizosphere microbial N limitation by enhancing extracellular enzyme activity.\u003c/p\u003e","manuscriptTitle":"Contrasting Rhizosphere Soil Stoichiometric Traits and Microbial Nitrogen Limitation between Maize and Peanut under Intercropping and Straw Retention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 18:24:09","doi":"10.21203/rs.3.rs-8696775/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae3ac947-8c0b-4bd3-9a05-b8cb79075238","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T12:21:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 18:24:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8696775","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8696775","identity":"rs-8696775","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}