Determining the distributions of soil carbon and nitrogen in particle size fractions using near-infrared reflectance spectrum of bulk soil samples

Barthès, Bernard; Brunet, Didier; Hien, Edmond; Enjalric, Frank; Conche, Sofian; Freschet, Grégoire T.; d’Annunzio, Rémi; Toucet-Louri, Joële

doi:10.1016/j.soilbio.2007.12.023

Cited by 67 publications

(41 citation statements)

References 14 publications

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“…This is thought to reflect the different sensitivity of FTIR to broad differences in SOM quality, like nonhumified vs humified OM . Likewise, FTIR calibration models of SOM content have found improved predictions with decreasing particle size, which are thought to be the result of coarser particles (>20 μm) having more heterogeneous SOM distribution (Barthès et al, 2008).…”

Section: Som Analysis Following Physical Fractionationmentioning

confidence: 99%

Soil Chemical Insights Provided through Vibrational Spectroscopy

Parikh

Goyne

Margenot

et al. 2014

Advances in Agronomy

221

163

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Section: Som Analysis Following Physical Fractionationmentioning

confidence: 99%

Soil Chemical Insights Provided through Vibrational Spectroscopy

Parikh

Goyne

Margenot

et al. 2014

Advances in Agronomy

221

163

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“…In addition, it is more adaptable to the requirement of realtime soil management and precision agriculture (Hummel et al, 2001;Maleki et al, 2008;Vasques and Grunwald, 2008). The main factors that influence soil spectra in the VNIR region include soil minerals composition (such as clay materials [Viscarra Rossel et al, 2006], iron bearing materials [Mortimore et al, 2004], and carbonates [Hunt and Salisbury, 1970]), soil moisture (Mouazen et al, 2006), soil surface (such as the color and particle size [Dunn et al, 2002;Barthes et al, 2008]), and SOM . The interaction between SOM content and spectral data forms the basis for SOM spectral models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

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“…Reeves et al (2006); Cozzolino and Moro (2006); Barthe et al (2008);and Vasques et al (2009) reported the same value of R2=0.76 in studies carried out in regions with high agroecological variability. But the accuracy is still well since the prediction is good (R2>0.75) according to the standard.…”

Section: Total Nitrogen (Tn)mentioning

confidence: 62%

Application of infrared technique in soil properties’ characterization in South Kivu province of DR Congo

Janvier¹

2015

Afr. J. Food Sci. Technol

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Understanding soil properties is an essential pre-requisite for sustainable land management. Assessment of soil properties has long been done through conventional laboratory analysis, which is costly and time consuming. Therefore, there is a need to develop alternative cheaper and faster techniques for soil analysis. In recent years, special attention has been given to Infrared Reflectance Spectroscopy and chemometrics. Near Infrared Reflectance (NIR) and mid-infrared (MIR) spectroscopy techniques are rapid, convenient and simple non-destructive techniques for quantifying several soil properties. This study aims to characterized soil based on based on infrared spectroscopy. This method were to predict soil pH, soil organic C, total N, exchangeable Al, Ca, Mg, and K, CEC and soil texture for soil samples collected in Sud-Kivu province, Democratic Republic of Congo. A total of 530 composite soil samples were taken from two locations (Burhale and Luhihi) at two depths (0-20 and 20-40 cm) using a spatially-stratified random sampling design within an area of 100 km 2 . After minimal sample preparation, the MIR spectrum of a soil takes about two minutes for the analyses. Ddifferences in characteristics were evaluated between the two locations, land use (cultivated vs. non-agricultural land) and soil depth. A random subset of the samples (10%) were analyzed using standard wet chemistry methods, and calibration models developed using MIR data to estimate soil properties for the full soil sample set. Partial least squares regression (PLS) method gave acceptable coefficients of determination between 0.71 and 0.93 for all parameters hence good prediction. Though IR is cheap for analyzing soil properties it requires high investment at the beginning. There is therefore need of technical and material support to make this technology useful in developing countries.

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Determining the distributions of soil carbon and nitrogen in particle size fractions using near-infrared reflectance spectrum of bulk soil samples

Cited by 67 publications

References 14 publications

Soil Chemical Insights Provided through Vibrational Spectroscopy

Soil Chemical Insights Provided through Vibrational Spectroscopy

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Application of infrared technique in soil properties’ characterization in South Kivu province of DR Congo

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