光谱学与光谱分析 |
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Assessment of Humic and Fulvic Acids in Black Soils Using Near-Infrared Reflectance Spectroscopy |
FAN Ru-qin1, 2, SHEN Yan1*, YANG Xue-ming3, ZHANG Xiao-ping1, LIANG Ai-zhen1, JIA Shu-xia1, CHEN Xue-wen1, WEI Shou-cai1, 2 |
1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China 3. Greenhouse and Processing Crops Research Centre, Agriculture and Agri-Food Canada, Harrow, Ontario, Canada N0R 1G0 |
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Abstract The organic carbon content and optical densities of humic acids in black soils of China were predicted and assessed using near infrared spectroscopy technique. The contents of humic acid (HA) and fulvic acid (FA) in 136 black soil samples in China were analyzed and the NIR spectra were collected using a VECTOR/22 (Fourier transform infrared spectroscopy). Partial least squares (PLS) regression with cross validation was used to develop prediction models with reference data and soil NIRS spectra, and the model was validated using an independent set of samples. NIRS well predicted (HAC+FAC), HAC and FAC contents, with R2 = 0.92, 0.92 and 0.86, RPD=3.66, 3.82 and 2.69, and high correlation coefficients between predicted and measured values (r=0.90, 0.85 and 0.82). Predictions for the E4 values of HA and FA were also good (R2=0.85, 0.85; RPD=2.88, 2.65; r=0.92, 0.80). Predictions for optical densities of HA and FA at 665 nm (E6) was acceptable. Generally, NIRS showed a good potential to predict C content and optical densities of humic acid and fulvic acid in blacks soils and may reveal information on SOC quality.
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Received: 2012-02-25
Accepted: 2012-05-28
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Corresponding Authors:
SHEN Yan
E-mail: yanshenfan@126.com
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[1] Stevens A, van Wesemael B, Vandenschrick G, et al. Soil Science Society of American Journal, 2006, 70: 844. [2] CHEN Zhen-hua, ZHANG Yu-lan, JIA Yin-hua, et al(陈振华, 张玉兰, 贾银华,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(1): 77. [3] Spaccini R, Piccolo A, Conte P, et al. Soil Biology & Biochemistry, 2002, 34: 1839. [4] Alborzfar M, Jonsson G, Grn C. Water Research, 1998, 32: 2983. [5] Xie H T, Yang X M, Drury C F, et al. Canadian Journal of Soil Science, 2011, 91: 53. [6] Schimann H, Joffre R, Roggy J C, et al. Applied Soil Ecology, 2007, 37: 223. [7] Cecillon L, Brun J J. In: Jandl R, Olsson M. (Eds.), Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Vienna, 2007, 103. [8] Terhoeven-Urselmans T, Schmidt H, Joergensen R G, et al. Soil Biology & Biochemistry, 2008, 40: 1178. [9] Chodak M, Khanna P, Beese F. Biology & Fertility of Soils, 2003, 39: 123. [10] Albrecht R, Le Petit J, Terrom G, et al. Bioresource Technology, 2011, 102: 4495. [11] Meissl K, Smidt E, Schwanninger M, et al. Applied Spectroscopy, 2008, 62: 873. [12] Liang A Z, Zhang X P, Yang X M, et al. European Journal of Soil Science, 2009, 60: 223. [13] LI You-kai(李酉开). Routine Analysis Methods for Soil Agricutural Chemistry(土壤农业化学常规分析方法). Beijing: Science Press(北京: 科学出版社), 1984. 404. [14] Saeys W, Mouazen A M, Ramon H. Biosystems Engineering, 2005, 91: 393. [15] Malley D F, Findlay D L, Zippel B. Journal of Paleolimnology, 1999, 21(5): 295. [16] Chang C W, Laird D A, Mausbach M J, et al. Soil Science Society of American Journal, 2001, 65: 480. [17] Ludwig B, Khanna P K, Bauhus J, et al. Forest Ecology and Management, 2002, 171: 121. [18] WEN Qi-xiao(文启孝). Analysis Methods of Soil Organic Matter(土壤有机质研究法). Beijing: China Agriculture Press(北京: 中国农业出版社), 1984. 107.
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