Welcome to Acta Agronomica Sinica，May. 6, 2025

Acta Agronomica Sinica ›› 2025, Vol. 51 ›› Issue (1): 273-284.doi: 10.3724/SP.J.1006.2025.43010

• RESEARCH NOTES • Previous Articles    

Effects of maize and soybean intercropping on soil physicochemical properties and microbial carbon metabolism in karst region

QIAN Yu-Ping1(), SU Bing-Bing2, GAO Ji-Xing1, RUAN Fen-Hua1, LI Ya-Wei3,*(), MAO Lin-Chun1,4   

  1. 1Pu’er Tea College, West Yunnan University of Applied Technology, Pu’er 665000, Yunnan, China
    2School of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
    3Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou 730070, Gansu, China
    4College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310000, Zhejiang, China
  • Received:2024-03-12 Accepted:2024-09-18 Online:2025-01-12 Published:2024-10-10
  • Contact: *E-mail: liyaweikl@163.com
  • Supported by:
    Pu’er Tea College, Western Yunnan University of Applied Technology(2023YJXM04);Joint Project of Local Universities in Yunnan Province(202301BA070001-066)

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Abstract:

This study aimed to investigate the effects of corn and soybean belt intercropping on soil physicochemical properties and microbial community structure diversity in a karst area. Three planting models were established: corn and soybean intercropping (MSI), corn monocropping (MM), and soybean monocropping (SM). The Biolog-ECO microplate method was used to explore the impacts of these different planting patterns on the metabolic activity, diversity, and soil properties of soil microbial carbon sources, as well as their underlying mechanisms. The results showed that compared to MM and SM, the MSI model significantly increased the soil microbial community richness index (McIntosh index) by 11.90% (P < 0.05) and 58.40% (P < 0.01), respectively, and the AWCD value by 24.50% and 80.10%, respectively. The relative absorbance of carboxylic acids, amino acids, and phenolic acids increased significantly by 34.50%, 63.70%, and 61.80% on average, respectively. The carbon source metabolic fingerprint revealed that the MSI model enhanced the utilization of p-carboxylic acid carbon sources by increasing the metabolic activity of itaconic acid, and improved the utilization of amino acid carbon sources by boosting the metabolic activity of L-phenylalanine, L-threonine, and glycyl-glutamic acid. Additionally, the MSI model increased the utilization of polymer carbon sources via enhanced metabolic activity of Tween 40, Tween 80, and liver sugar. Furthermore, soil SOC under MSI treatment was significantly higher by 8.50% and 72.84% compared to MM and SM, respectively, while NH4+-N and TN contents were significantly increased by 46.70% and 33.30% compared to SM treatment, respectively. Principal component analysis revealed that the two extracted components explained 79.69% of the total variation in carbon source utilization. The overall carbon source metabolic capacity followed the order MSI > MM > SM, with the MSI soil microbial community demonstrating the strongest metabolic utilization of carboxylic acids, amino acids, and polymers. Redundancy analysis indicated that TN (53.50%) and SOC (30.90%) were the two most significant environmental factors influencing carbon source metabolic utilization. TN promoted the metabolic utilization of carboxylic acid and amino acid carbon sources, while SOC enhanced the utilization of amine and phenolic acid carbon sources. The preferential carbon metabolism observed in maize and soybean intercropping was primarily driven by the diversity of microbial community structure, and was further regulated by soil total nitrogen and organic matter content. These findings suggest that the interaction between microbial community structure and soil physicochemical properties may play a key role in the yield improvement and efficiency of soybean and corn intercropping systems.

Key words: maize, soybean, soil microorganism, metabolic activity of carbon source, physical and chemical properties of soil, Biolog-ECO

Fig. 1

Rainfall and average daily temperature at the experimental site during the crop growth period"

Fig. 2

Diagram of field planting patterns MM: maize monoculture; MSI: maize and soybean intercropping; SM: soybean monoculture."

Table 1

Effects of planting patterns on soil physicochemical factors"

种植模式
Cropping pattern		 土壤含水量
SWC (%)		 pH 土壤温度
ST (℃)		 有机质含量
SOC (g kg-1)		 NO3--N
(mg kg-1)		 NH4+-N
(mg kg-1)		 全氮含量
TN (g kg-1)
MSI 25.64±0.01 b 5.83±0.05 b 24.25±0.31 b 33.15±3.23 a 12.82±1.78 b 38.63±4.08 a 0.88±0.04 a
MM 29.32±0.06 a 5.92±0.09 ab 25.13±0.63 b 30.56±2.56 b 4.98±2.72 c 34.40±8.36 ab 0.80±0.05 a
SM 27.06±0.03 ab 6.13±0.16 ab 26.42±0.85 a 19.18±3.28 c 21.23±1.27 a 26.34±4.27 b 0.66±0.04 b

Fig. 3

Average well color development of soil microbial community light absorption value after 168 hours of culture (mean±SD) AWCD value: average well color development. Different lowercase letters represent the average values of the corresponding indicators in different planting patterns, and there are significant differences when Duncan’s method is used for multiple comparison (P < 0.05). Treatments are the same as those given in Fig. 2."

Fig. 4

Effects of planting patterns on the relative absorbance values of six types of carbon source metabolism in soil microbial communities CH: carbohydrates; CA: carboxylic acids; AA: amino acids; PM: polymers; AM: amines; PA: phenolic acids. Treatments are the same as those given in Fig. 2."

Table 2

Relative proportions of metabolic utilization of six types of carbon sources in soil microbial communities under different planting patterns"

种植模式
Cropping pattern		 碳水化合物类Carbohydrates 羧酸类
CA		 氨基酸类
AA		 多聚类
PM		 胺类
AM		 酚酸类
PA
MSI 27.98±2.20 b 21.19±0.69 a 25.71±0.61 a 16.98±0.28 a 5.07±1.88 a 3.07±0.77 b
MM 39.02±2.35 a 20.91±1.77 a 22.77±2.03 a 12.72±2.20 b 2.96±1.99 a 1.62±0.43 c
SM 29.65±1.54 b 22.37±3.43 a 19.71±3.58 a 16.49±1.04 a 6.80±2.19 a 4.98±0.50 a

Table 3

Principal component analysis of metabolic utilization of six types of carbon sources by soil microbial communities under different planting patterns"

主成分数
Principal component number		 特征值
Eigenvalue		 方差贡献率
Percentage of variance (%)		 累计贡献率
Cumulative (%)
1 3.64 60.75 60.75
2 1.14 18.95 79.69
3 0.84 14.06 93.75
4 0.28 4.74 98.49
5 0.08 1.30 99.78
6 0.01 0.22 100.00

Fig. 5

Principal component analysis of carbon source utilization characteristics of planting patterns PC1: principal component 1; PC2: principal component 2. Abbreviations are the same as those given in Fig. 4."

Table 4

Main component score coefficient and comprehensive score of soil microbial community’s carbon source utilization characteristics in different planting patterns"

处理
Treatment		 PC1 PC2 综合得分
Synthesis score		 排名
Ranking
MSI 2.27 0.42 1.46 1
MM -0.26 -1.13 -0.37 2
SM -2.01 0.71 -1.09 3

Fig. 6

Fingerprint of metabolic characteristics of soil microbial communities to 31 single carbon sources under different planting patterns A3: D-galactonic acid γ-Lactone; A2: β-methyl-D-glucoside; G1: D-cellobiose; H1: α-D-lactose; C2: i-erythritol; G2: α-D-glucose- 1-phosphate; B2: D-xylose; D2: D-mannitol; E2: N-acetyl-D-glucosamine; H2: D,L-α-glycerol phosphate; B3: D-galacturonic acid; F2: D-glucosaminic Acid; B4: L-asparagine; C4: L-phenylalanine; A4: L-arginine; D4: L-serine; E4: L-threonine; F4: glycyl-L-glutamic acid; E3: γ-hydroxybutyric acid; F3: itaconic acid; G3: α-ketobutyric acid; H3: D-malic acid; B1: pyruvic acid methyl ester; E1: α-cyclodextrin;F1: glycogen; C1: tween 40; D1: tween 80; C3: 2-hydroxy benzoic acid; D3: 4-hydroxy benzoic acid; G4: phenylethylamine; H4: putrescine.Different lowercase letters indicate significant difference between the corresponding means when using Duncan’s multiple comparisons (P < 0.05). Treatments are the same as those given in Fig. 2."

Table 5

Effects of planting patterns on soil microbial community diversity index"

处理
Treatment		 香农指数
Shannon index		 辛普森指数
Simpson index		 均匀度指数
Evenness index		 丰富度指数
McIntosh index
MSI 3.29±0.073 a 0.96±0.002 a 0.97±0.009 a 5.56±0.096 a
MM 3.12±0.206 a 0.95±0.003 b 0.94±0.046 a 4.97±0.118 b
SM 3.06±0.127 a 0.95±0.004 b 0.95±0.036 a 3.51±0.128 c

Fig. 7

Correlation analysis between soil physicochemical factors and soil microbial carbon source utilization characteristics and diversity H: Shannon index; D: Simpson index; E: Evenness index; U: McIntosh index. * indicates that the P-value is less than 0.05, and ** indicates that the P-value is less than 0.01. Abbreviations are the same as those given in Table 1 and Fig. 6."

Fig. 8

Redundancy analysis of soil physical and chemical properties and soil microbial community utilization characteristics of six types of carbon sources The red arrows represent environmental factors and the blue arrows represent the six types of carbon sources. RDA1 and RDA2 repre-sent the first and second axes of redundancy analysis, respectively. Each dot represents a sample, and different colored dots belong to different planting patterns. Treatments are the same as those given in Fig. 2. Abbreviations are the same as those given in Table 1 and Fig. 4."

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