基于3DEEM-PARAFAC研究连作花生不同健康状态下根际有机质组成特性

doi:10.3964/j.issn.1000-0593(2022)02-0634-08

Abstract
References
Related Citation (15)

Download: PDF (4633 KB)
Export: BibTeX | EndNote (RIS)

Abstract The soil-borne disease of continuous cropping peanut is serious, but the internal relationship between the occurrence of soil-borne disease and soil factors, especially the dissolved organic matter (DOM) composition of rhizosphere soil, is still unclear. In order to explore the effect of peanut diseases on the rhizosphere soil DOM composition, the rhizosphere soils of healthy and diseased peanut plants were collected from multiple locations in Yu Jiang county. Three-dimensional excitation-emission matrix (3DEEM) and parallel factor method (PARAFAC) were used to analyze the variations of DOM compositions among rhizosphere soils of diseased and healthy peanut plants. Results showed no significant difference in the basic properties of rhizosphere soil between healthy peanut and diseased peanut. Five DOM components, including tryptophan-like (C1), fulvic-like (C2), microbial-humic-like (C3), humic-like (C4) and tyrosine-like (C5) were identified, and the variations of DOM fluorescence component composition in the rhizosphere soil between healthy peanut and diseased peanut were significantly different. The tryptophan-like (C1) in the rhizosphere soil of healthy plants accounted for 53.79%, which was significantly higher than 25.72% in diseased plants, while the opposite trend appeared in other components; The BIX and HIX of DOM in the rhizosphere soil of healthy peanut were (0.95±0.03) and (1.87±0.25), respectively, which were significantly higher than (0.82±0.02) and (0.98±0.09) of diseased peanut. Higher BIX and HIX values could be an intrinsic signature to rhizosphere environment keeping healthy. The Principal Co-ordinates Analysis showed that the healthy group and the diseased group could be effectively differentiated by the fluorescence components characterized with the application of 3DEEM-PARAFAC. A significant correlation was found between peanut biomass and each component of DOM by Correlation Analysis. Furthermore, peanut biomass showed a significantly positive correlation with BIX and HIX, while the Mcknight index was only closely related to some soil properties. The Variance Partitioning Analysis showed that the explanation rate of peanut biomass to the variation of DOM composition was up to 40%. However, Soil properties could not significantly explain the variation of DOM composition, indicating that peanut growth status is an important factor affecting the DOM composition of rhizosphere soil. In summary, there is a correlation between peanut health and DOM composition with fluorescence characteristics of rhizosphere soil, which can provide a theoretical reference to understand the pathogenesis of peanut soil-borne diseases and guide the establishment of relevant scientific control schemes.

Key words：Continuous cropping peanuts; Health status; Rhizosphere soil; Dissolved organic matter; Three-dimensional excitation-emission matrices; Parallel factor analysis

Received: 2020-12-31 Accepted: 2021-02-04

ZTFLH:

S153.6+2

Corresponding Authors: LI Zhong-pei E-mail: zhpli@issas.ac.cn

Cite this article:

LIU Tian-shun,LI Peng-fa,LI Gui-long, et al. Using Three-Dimensional Excitation-Emission Matrix to Study the Compositions of Dissolved Organic Matter in the Rhizosphere Soil of Continuous Cropping Peanuts With Different Health States[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 634-641.

URL:

http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)02-0634-08 OR http://www.gpxygpfx.com/EN/Y2022/V42/I02/634

[1] Liu J, Chen X F, Li D, et al. Journal of Soils and Sediments, 2020, 20(7): 2761.
[2] Kalbitz K, Schmerwitz J, Schwesig D, et al. Geoderma, 2003, 113(3-4): 273.
[3] Musadji N Y, Lemee L, Caner L, et al. Chemosphere, 2020, 240: 124808.
[4] TENG Ying, REN Wen-jie, LI Zhen-gao, et al(滕应, 任文杰, 李振高, 等). Soils(土壤), 2015, 47(2): 259.
[5] Del Valle I, Webster T M, Cheng H Y, et al. Sci. Adv., 2020, 6(5): 12.
[6] Zhang Y L, Zhang E L, Yin Y, et al. Limnology and Oceanography, 2010, 55(6): 2645.
[7] LU Ru-kun(鲁如坤). Analytical Methods for Soil and Agricultural Chemistry(土壤农业化学分析方法). Beijing: China Agricultural Science and Technology Press(北京: 中国农业科学技术出版社)，1999.
[8] Novara A, La Mantia T, Ruhl J, et al. Geoderma, 2014, 235: 191.
[9] Santos C H, Nicolodelli G, Romano R A, et al. J. Brazil. Chem. Soc., 2015, 26(6): 1136.
[10] McKnight D M, Boyer E W, Westerhoff P K, et al. Limnology and Oceanography, 2001, 46(1): 38.
[11] LIU Xiao-han, ZHANG Yun-lin, YIN Yan, et al(刘笑菡, 张运林, 殷燕, 等). Transactions of Oceanology and Limnology(海洋湖沼通报), 2012, 134(3): 133.
[12] Stedmon C A, Bro R. Limnology and Oceanography: Methods, 2008, 6(11): 572.
[13] Coble P G. Chem. Rev., 2007, 107(2): 402.
[14] Shi J, Zhao Y, Wei D, et al. Environ. Sci. Pollut. Res. Int., 2019, 26(4): 3340.
[15] Scholthof K B. Nat. Rev. Microbiol., 2007, 5(2): 152.
[16] Fellman J B, Hood E, Spencer R G M. Limnology and Oceanography, 2010, 55(6): 2452.
[17] Crits-Christoph A, Diamond S, Butterfield C N, et al. Nature, 2018, 558(7710): 440.
[18] Wei Z, GU Y, Friman V P, et al. Science Advances, 2019, 5(9): eaaw0759.
[19] ZHU Zhen-ke, SHEN Bing-jie, GE Ti-da, et al(祝贞科, 沈冰洁, 葛体达, 等). Acta Ecologica Sinica(生态学报), 2016, 36(19): 5987.
[20] Liu H, Li J, Carvalhais L C, et al. New Phytologist, 2021, 229(5): 2873.