Predicting Soil Carbon and Nitrogen Concentrations and Pasture Root Densities from Proximally Sensed Soil Spectral Reflectance

Kusumo, Bambang Hari; Hedley, M. J.; Tuohy, M. P.; Hedley, C. B.; Arnold, Gregory C.

doi:10.1007/978-90-481-8859-8_15

Cited by 9 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition lower clay content in the fine sandy loam may create lower soil strength (Mathers et al 1966) with less mechanical impedance to root growth (Unger and Kaspar 1994). The total C and N concentrations in these regularly cultivated soils (maximum of 3.5% C and 0.33% N in the silt loam; Table 1) were lower than those found in the same soil type under permanent pasture (maximum Kusumo et al 2008b). Rapid soil organic matter (SOM) decline occurs in these soils when permanent pastures are converted to maize/annual ryegrass rotations (Shepherd et al 2001).…”

Section: Discussionmentioning

confidence: 80%

“…Vis-NIR reflectance spectroscopy has been successfully used to predict soil C (e.g. Chang and Laird 2002;Kusumo et al 2008a), ryegrass root density in a glasshouse experiment (Kusumo et al 2009a) and in field trials (Kusumo et al 2009b), despite variations in soil texture, structure (Udelhoven et al 2003) and other chromophores (e.g. clay and non-clay contents, iron oxides, water, decomposed organic matter) (Baumgardner et al 1985;Ben-Dor 2002).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

Kusumo

Hedley

et al. 2010

Plant Soil

View full text Add to dashboard Cite

This paper reports the development of a proximal sensing technique used to predict maize root density, soil carbon (C) and nitrogen (N) content from the visible and near-infrared (Vis-NIR) spectral reflectance of soil cores. Eighteen soil cores (0-60 cm depth with a 4.6 cm diameter) were collected from two sites within a field of 90-day-old maize silage; Kairanga silt loam and Kairanga fine sandy loam (Gley Soils). At each site, three replicate soil cores were taken at 0, 15 and 30 cm distance from the row of maize plants (rows were 60 cm apart). Each soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60 cm) and soil reflectance spectra were acquired from the freshly cut surface at each depth. A 1.5 cm soil slice was taken at each surface to obtain root mass and total soil C and N reference (measured) data. Root densities decreased with depth and distance from plant and were lower in the silt loam, which had the higher total C and N contents. Calibration models, developed using partial least squares regression (PLSR) between the first derivative of soil reflectance and the reference data, were able to predict with moderate accuracy the soil profile root density (r 2 =0.75; ratio of prediction to deviation [RPD]=2.03; root mean square error of crossvalidation [RMSECV] = 1.68 mg/cm 3 ), soil% C (r 2 =0.86; RPD=2.66; RMSECV=0.48%) and soil% N (r 2 =0.81; RPD=2.32; RMSECV=0.05%) distribution patterns. The important wavelengths chosen by the PLSR model to predict root density were different to those chosen to predict soil C or N. In addition, predicted root densities were not strongly autocorrelated to soil C (r=0.60) or N (r=0.53) values, indicating that root density can be predicted independently from soil C. This research has identified a potential method for assessing root densities in field soils enabling study of their role in soil organic matter synthesis.

show abstract

Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

Kusumo

Hedley

et al. 2010

Plant Soil

View full text Add to dashboard Cite

show abstract

“…Ben-Dor et al (2008) developed a so-called penetrating optical sensor to measure soil properties down the soil profile to provide data that can be used for soil type classification. Kusumo et al (2010) reported good calibration quality for C and N (RPD ¼ 2.90-5.80) from spectral measurements taken from the vertical side of a vertically extracted soil core sampled at six depths from 15 to 315 mm. However, spectra sampled from the horizontal cross-sections of 5 cm slices gave slightly less accurate predictions.…”

Section: Field Analysesmentioning

confidence: 99%

Visible and Near Infrared Spectroscopy in Soil Science

Stenberg

Rossel

Mouazen

et al. 2010

Advances in Agronomy

1,064

845

View full text Add to dashboard Cite

This chapter provides a review on the state of soil visible-near infrared (vis-NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis-NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis-NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis-NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis-NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis-NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic.

show abstract

“…NIR has also been used in the field. Kusumo et al 19 have demonstrated a fieldable NIR-based reflectance probe for determining total nitrogen from soil cores. 20 Determining nitrate levels using NIR, however, has been less successful.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Sensing of Soil Nitrate Concentration in the Parts per Million Range While the Soil is in Motion

et al. 2013

View full text Add to dashboard Cite

Reactive nitrogen (Nr) is a term used to describe non-nitrogen gas (non-N2) forms of nitrogen (N) in the biosphere. It causes major pollution problems when it occurs in excess, and it has many sources, including fertilizers used in production agriculture. Currently there is no on-the-go soil nitrate sensor that could guide the application of the optimal amount of fertilizer, which often varies significantly within a field. We report for the first time nitrate-in-soil measurements performed on moving soil samples at concentration levels relevant for fertilizer application. An infrared emission technique called transient infrared spectroscopy (TIRS) was tested on soil samples spiked with different nitrate concentrations in the parts-per-million range and moving at a velocity of 2.6 m/s (5.8 miles per hour) in the laboratory. The TIRS Fourier transform infrared (FT-IR) spectra were modeled by partial least squares and produced a standard error of cross-validation (SECV) of 6.3 parts per million (ppm) N and an R 2 of 0.938 for 512-scan spectra. These results are compared to those using fewer TIRS scans and to those from photoacoustic spectroscopy (PAS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements on stationary samples. TIRS 128-, 32-, and 8-scan spectra yielded SECVs of 11.2, 11.4, and 18.4 ppm N and R 2 values of 0.800, 0.831, and 0.583, respectively. The PAS and DRIFTS measurements produced SECVs of 12.4 and 9.0 ppm N and R 2 values of 0.766 and 0.876, respectively. Reactive nitrogen (Nr) is a term used to describe non-nitrogen gas (non-N 2 ) forms of nitrogen (N) in the biosphere. It causes major pollution problems when it occurs in excess, and it has many sources, including fertilizers used in production agriculture. Currently there is no on-the-go soil nitrate sensor that could guide the application of the optimal amount of fertilizer, which often varies significantly within a field. We report for the first time nitrate-in-soil measurements performed on moving soil samples at concentration levels relevant for fertilizer application. An infrared emission technique called transient infrared spectroscopy (TIRS) was tested on soil samples spiked with different nitrate concentrations in the parts-per-million range and moving at a velocity of 2.6 m/s (5.8 miles per hour) in the laboratory. The TIRS Fourier transform infrared (FT-IR) spectra were modeled by partial least squares and produced a standard error of cross-validation (SECV) of 6.3 parts per million (ppm) N and an R 2 of 0.938 for 512-scan spectra. These results are compared to those using fewer TIRS scans and to those from photoacoustic spectroscopy (PAS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements on stationary samples. TIRS 128-, 32-, and 8-scan spectra yielded SECVs of 11.2, 11.4, and 18.4 ppm N and R 2 values of 0.800, 0.831, and 0.583, respectively. The PAS and DRIFTS measurements produced SECVs of 12.4 and 9.0 ppm N and R 2 values of 0.766 and 0.876, respective...

show abstract

Predicting Soil Carbon and Nitrogen Concentrations and Pasture Root Densities from Proximally Sensed Soil Spectral Reflectance

Cited by 9 publications

References 13 publications

Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

Measuring carbon dynamics in field soils using soil spectral reflectance: prediction of maize root density, soil organic carbon and nitrogen content

Visible and Near Infrared Spectroscopy in Soil Science

Real-Time Sensing of Soil Nitrate Concentration in the Parts per Million Range While the Soil is in Motion

Contact Info

Product

Resources

About