Dataset Information

Intercropping With Aromatic Plants Increased the Soil Organic Matter Content and Changed the Microbial Community in a Pear Orchard.

ABSTRACT: Intercropping influences the soil microbiota via litter and root exudate inputs, but the mechanisms by which root exudates mediate the soil microbial community and soil organic matter (SOM) are still unclear. In this study, we selected three aromatic plants (Ocimum basilicum, Tr1; Satureja hortensis, Tr2; Ageratum houstonianum, Tr3) as intercrops that separately grew between rows of pear trees, and no plants were grown as the control in a pear orchard during the spring-summer season for 3 years. The soil from each plot was collected using a stainless-steel corer by five-point sampling between rows of pear trees. The bacterial and fungal communities of the different aromatic intercrops were analyzed by 16S and ITS rRNA gene amplicon sequencing; their functional profiles were predicted by PICRUSt and FUNGuild analyses. The root exudates of the aromatic plants were analyzed by a liquid chromatography-tandem mass spectrometry (LC-MS) system. Compared with the control treatment, all intercropping treatments with aromatic plants significantly increased SOM and soil water content and decreased pH values. The contents of total nitrogen and alkali-hydrolyzable nitrogen in Tr1 and Tr2 were higher than those in Tr3. In Tr3 soil, the relative content of saccharides increased little, whereas the changes in amine (increases) and alcohols (decreases) were rapid. Ageratum houstonianum intercropping decreased the microbial community diversity and significantly influenced the relative abundances of the dominant microbiota (Actinobacteria, Verrucomicrobia, Gemmatimonadetes, Cyanobacteria, Ascomycota, and Basidiomycota) at the phylum, class, and order levels, which increased the assemblage of functional groups (nitrite ammonification, nitrate ammonification, and ureolysis groups). Our study suggested that the main root exudates from aromatic plants shaped the microbial diversity, structure, and functional groups related to the N cycle during SOM mineralization and that intercropping with aromatic plants (especially basil and summer savory) increased N release in the orchard soil.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7907656 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Intercropping With Aromatic Plants Increased the Soil Organic Matter Content and Changed the Microbial Community in a Pear Orchard.

Zhang Yan Y Han Mingzheng M Song Mengni M Tian Ji J Song Beizhou B Hu Yujing Y Zhang Jie J Yao Yuncong Y

Frontiers in microbiology 20210212

Intercropping influences the soil microbiota via litter and root exudate inputs, but the mechanisms by which root exudates mediate the soil microbial community and soil organic matter (SOM) are still unclear. In this study, we selected three aromatic plants (Ocimum basilicum, Tr1; Satureja hortensis, Tr2; Ageratum houstonianum, Tr3) as intercrops that separately grew between rows of pear trees, and no plants were grown as the control in a pear orchard during the spring-summe ...[more]

PMID: 33643243

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Project description:BackgroundThe sugarcane/peanut intercropping system is a specific and efficient cropping pattern in South China. Intercropping systems change the bacterial diversity of soils and decrease disease rates. It can not only utilized light, heat, water and land resources efficiently, but also increased yield and economic benefits of farmers.MethodsWe determined soil nutrients, enzymes and microbes in sugarcane/peanut intercropping system, and analyzed relevance of the soil physicochemical properties and the genes involved in N and P cycling and organic matter turnover by metagenome sequencing.ResultsThe results showed that sugarcane/peanut intercropping significantly boosted the content of total nitrogen, available phosphorus, total potassium, organic matter, pH value and bacteria and enhanced the activity of acid phosphatase compared to monocropping. Especially the content of available nitrogen, available phosphorus and organic matter increased significantly by 20.1%, 65.3% and 56.0% in root zone soil of IP2 treatment than monocropping treatment. The content of available potassium and microbial biomass carbon, as well as the activity of catalase, sucrase and protease, significantly decreased in intercropping root zone soil. Intercropping resulted in a significant increase by 7.8%, 16.2% and 23.0% in IS, IP1 and IP2, respectively, of the acid phosphatase content relative to MS. Metagenomic analysis showed that the pathways involved in carbohydrate and amino acid metabolism were dominant and more abundant in intercropping than in monocropping. Moreover, the relative abundances of genes related to N cycling (glnA, GLUD1_2, nirK), P cycling (phoR, phoB) and organic matter turnover (PRDX2_4) were higher in the intercropping soil than in the monocropping soil. The relative abundance of GLUD1_2 and phoR were 25.5% and 13.8% higher in the IP2 treatment respectively,and bgIX was higher in IS treatment compared to the monocropping treatment. Genes that were significantly related to phosphorus metabolism and nitrogen metabolism (TREH, katE, gudB) were more abundant in intercropping than in monocropping.ConclusionThe results of this study indicate that the intercropping system changed the numbers of microbes as well as enzymes activities, and subsequently regulate genes involved in N cycling, P cycling and organic matter turnover. Finally, it leads to the increase of nutrients in root zone soil and improved the soil environment.

Project description:Soil organic matter (SOM) is a key index of soil fertility. Calculating spectral index and screening characteristic band reduce redundancy information of hyperspectral data, and improve the accuracy of SOM prediction. This study aimed to compare the improvement of model accuracy by spectral index and characteristic band. This study collected 178 samples of topsoil (0-20 cm) in the central plain of Jiangsu, East China. Firstly, visible and near-infrared (VNIR, 350-2500 nm) reflectance spectra were measured using ASD FieldSpec 4 Std-Res spectral radiometer in the laboratory, and inverse-log reflectance (LR), continuum removal (CR), first-order derivative reflectance (FDR) were applied to transform the original reflectance (R). Secondly, optimal spectral indexes (including deviation of arch, difference index, ratio index, and normalized difference index) were calculated from each type of VNIR spectra. Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. Meanwhile, SOM prediction models were established using characteristic wavelengths, denoted here as CARS-based models. Finally, this research compared and assessed accuracy of SI-based models and CARS-based models, and selected optimal model. Results showed: (1) The correlation between optimal spectral indexes and SOM was enhanced, with absolute value of correlation coefficient between 0.66 and 0.83. The SI-based models predicted SOM content accurately, with the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.80 to 0.87, 2.40 g/kg to 2.88 g/kg in validation sets, and relative percent deviation (RPD) value between 2.14 and 2.52. (2) The accuracy of CARS-based models differed with models and spectral transformations. For all spectral transformations, PLSR and SVR combined with CARS displayed the best prediction (R2 and RMSE values ranged from 0.87 to 0.92, 1.91 g/kg to 2.56 g/kg in validation sets, and RPD value ranged from 2.41 to 3.23). For FDR and CR spectra, DNN and RF models achieved more accuracy (R2 and RMSE values ranged from 0.69 to 0.91, 1.90 g/kg to 3.57 g/kg in validation sets, and RPD value ranged from 1.73 to 3.25) than LR and R spectra (R2 and RMSE values from 0.20 to 0.35, 5.08 g/kg to 6.44 g/kg in validation sets, and RPD value ranged from 0.96 to 1.21). (3) Overall, the accuracy of SI-based models was slightly lower than that of CARS-based models. But spectral index had a good adaptability to the models, and each SI-based model displayed the similar accuracy. For different spectra, the accuracy of CARS-based model differed from modeling methods. (4) The optimal CARS-based model was model CARS-CR-SVR (R2 and RMSE: 0.92 and 1.91 g/kg in validation set, RPD: 3.23). The optimal SI-based model was model SI3-SVR (R2 and RMSE: 0.87 and 2.40 g/kg in validation set, RPD: 2.57) and model SI-SVR (R2 and RMSE: 0.84 and 2.63 g/kg in validation set, RPD: 2.35).

Dataset Information

Intercropping With Aromatic Plants Increased the Soil Organic Matter Content and Changed the Microbial Community in a Pear Orchard.

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Intercropping With Aromatic Plants Increased the Soil Organic Matter Content and Changed the Microbial Community in a Pear Orchard.

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