Use of near-infrared reflectance spectroscopy to predict the percentage of dead versus living grass roots

Picon‐Cochard, Catherine; Pilon, Rémi; Revaillot, Sandrine; Jestin, Michel; Dawson, Lorna

doi:10.1007/s11104-008-9810-2

Cited by 18 publications

(19 citation statements)

References 48 publications

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“…This can be a useful when information is required about the potential for deeper rooting species to synthesize organic matter and sequester atmospheric CO 2 (Rees et al 2005); particularly as the technique has been proven able to simultaneously predict root density, soil C and soil N, both in permanent pasture (Kusumo et al 2009b) and arable land (in this study). In addition, NIRS has been reported to be able to measure live and dead grass roots (Picon-Cochard et al 2009). …”

Section: Discussionmentioning

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

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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.

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Section: Discussionmentioning

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

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“…For root measurement, NIRS has been applied successfully in quantifying herbaceous species (Rumbaugh et al 1988;Roumet et al 2006) and tree species (Lei & Bauhus 2010) composition of fine root mixtures, and in determining the percentage of dead and live root sample (Picon-Cochard et al 2009). The number of species contained in fine root mixtures for NIRS measurement ranged from two to five plant compositions, but the predictive quality of established NIRS models did not decrease with increasing complexity of the root samples (Lei & Bauhus 2010).…”

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confidence: 99%

Prediction of tree species composition in fine root mixed samples using near-infrared reflectance spectroscopy

Tong

Xiang

Liu

et al. 2014

Plant Biosystems - An International Journal Dealing with all As

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Fine roots (# 2 mm diameter) are of great value when investigating belowground interactions among different plant species and soil nutrient cycling in forest ecosystems. However, fine root separation and species identification are labor-intensive and time-consuming processes. This study aimed to evaluate the aptitude of near-infrared reflectance spectroscopy (NIRS) in predicting tree species composition in fine root mixed samples. The coniferous species Cunninghamia lanceolata and Pinus massoniana, the deciduous species Alniphyllum fortunei and Liquidambar formosana, and the evergreen broadleaved species Cyclobalanopsis glauca represent the five subtropical tree species selected for this investigation. To obtain near-infrared reflectance spectral data, 20 samples taken in the field and 70 artificially mixed samples of the five species were produced after root samples were oven-dried and ground. Calibration was performed with partial least squares regression and leave-one-out cross-validation. Root mass proportions of the mixed samples showed good predictive capacity for C. lanceolata, P. massoniana, and C. glauca with low root mean square error of prediction (, 6.82%) and high determination coefficients (R 2 . 0.944). Predictions for A. fortunei and L. formosana were acceptable with R 2 . 0.819. NIRS shows potential in predicting tree species composition with suitable accuracy.

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“…In hybrid poplar cuttings, VIS-NIR (356-976 nm) spectral images allow accurate root classification (number of roots), as reflectance decreases with root ageing, regardless of soil moisture conditions. In roots extracted from core samples of various grass species, such as Anthoxanthum odoratum Webb and Brethel., Dactylis glomerata L., Festuca arundinacea Schreb., Festuca rubra L. and Lolium perenne L., Picon-Cochard et al (2009) also achieved a very low classification error (only 15%) using near-infrared reflectance spectroscopy (NIRS).…”

Section: Living Vs Dead Roots: the Minirhizotron's Point Of Viewmentioning

confidence: 99%

Minirhizotrons in Modern Root Studies

2011

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In recent years, minirhizotrons have received increasing interest in field studies for characterising several biological processes, such as fine root production, root longevity, mycorrhization and parasitism, as collecting repeated video or digital images allows the fate of individual roots to be followed in time.This review summarises recent technical improvements in root observation and imaging, comparing results from various construction materials and installation angles. Both glass and transparent acrylic or polycarbonate tubes have shown a relatively low influence on root behaviour; tilted tubes are preferred to vertically oriented ones, in order to avoid artefacts, and to horizontal ones, which involve considerable soil disturbance during positioning in their surroundings in the open field. Precise camera positioning systems and new high-resolution (600 DPI and more) digital sensors are currently replacing external-tracked tubes and analog sensors, enhancing image capturing and quality. This allows for frequent root observation throughout the plant cycle and seasons, required for accurate estimation of root dynamics.Although manual and semi-automatic image analysis requires timing and tedious work, in the last few years minirhizotron systems have been increasingly used, thanks to fundamental advances in image analysis. The development of new algorithms for root detection and measurement in minirhizotron images has speeded up processing and research. The most interesting results in image analysis derive from the integration of luminance intensity-and geometry-based root vs. non-root classifiers. Discrepancies still remain between minirhizotron data and

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Use of near-infrared reflectance spectroscopy to predict the percentage of dead versus living grass roots

Cited by 18 publications

References 48 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

Prediction of tree species composition in fine root mixed samples using near-infrared reflectance spectroscopy

Minirhizotrons in Modern Root Studies

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