Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics

Gislum, René; Micklander, Elisabeth; Nielsen, Jesper Pram

doi:10.1016/j.fcr.2004.01.021

Cited by 94 publications

(45 citation statements)

References 15 publications

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“…Using three different application rates of urine (0, 300, and 700 kg N ha −1 ) on soil from the same area as the present study Moir and colleagues (Moir, Edwards, et al 2012;Moir, Malcolm, et al 2012) recorded comparable mean N concentrations in ryegrass of 2.65-4.55%. Similarly, in extensive field trials of L. perenne and Festuca rubra in Denmark, Gislum et al (2004) reported a range of 0.6 to 6.26% N (mean value 2.81%). Clearly, luxury uptake of N is not characteristic of native plants.…”

Section: Nursery-plant Foliagementioning

confidence: 92%

Endemic Plants as Browse Crops in Agricultural Landscapes of New Zealand

Dickinson

Marmiroli

Das

et al. 2014

Agroecology and Sustainable Food Systems

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We investigated the benefits of restoring native plants to intensive agricultural landscapes of New Zealand. As a browse crop on paddock borders, endemic species potentially could add value to more sustainable food systems. Their foliage was found to contain less nitrogen but higher concentrations of a range of trace elements and tannins than both ryegrass and border-planted willows. While native plants could reduce the need for feed supplements, we conclude that the small amount consumed would provide negligible overall nutritional benefits to stock. Nevertheless, this may provide added justification for endemic species and agroecology in landscapes with a depauperate native biodiversity.

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Section: Nursery-plant Foliagementioning

confidence: 92%

Endemic Plants as Browse Crops in Agricultural Landscapes of New Zealand

Dickinson

Marmiroli

Das

et al. 2014

Agroecology and Sustainable Food Systems

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“…PLSR is a powerful method commonly used to develop calibration equations for predicting the variables of interest for a sample from NIR spectral data (Richardson et al 2003). The advantages of PLSR are that not only all NIR spectral data are included in the calibration Equations (Gislum et al 2004), but also variance of the data is extracted and related to the predicted variables (Richardson et al 2003). In small samples, cross-validation offers slightly superior predictive performance than external test validations (Martens & Dardenne 1998;Moron & Cozzolino 2004).…”

Section: Prediction Of Fine Root Proportions (Hypothesis 2)mentioning

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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“…Some factors such as growing season and growing location result in different environmental factors that may affect the composition of the samples and decrease the accuracy of the calibrations. These influences are well known in the case of grass mixtures where the composition of samples may change from one season to another depending on the percentage of each grass (García Ciudad et al, 1999;Gislum et al, 2004). In the case of grains, it has been also observed an influence of the environmental conditions on the developing of NIRS calibrations in triticale (Igne et al, 2007) and mustard (Velasco et al, 1997).…”

Section: Discussionmentioning

confidence: 85%

Application of near-infrared reflectance spectroscopy for the estimation of protein and L-canavanine contents in seeds of one-flowered vetch (Vicia articulata Hornem.)

Vioque¹,

Conde²,

Pascual³

2009

Span. j. agric. res.

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In this work it has been evaluated the effectiveness of near-infrared reflectance spectroscopy (NIRS) for the estimation of two constituents affecting the nutritional quality of the seeds of one-flowered vetch (Vicia articulata Hornem.): protein and L-canavanine, a toxic non-protein amino acid. The NIRS calibrations showed good statistics with coefficients of multiple determination (R 2 ) of 0.97 for total protein and 0.95 for L-canavanine. The developed equations were further used to estimate the contents of protein and L-canavanine of a set of unknown samples (external validation). The equation for total protein was able to predict with an accuracy similar to that of the reference method showing a correlation (r 2 ) between the reference and predicted values of 0.95. In the case of L-canavanine, r 2 was only 0.72 and the equation was only effective to discriminate samples into groups of low, medium and high contents. Additional key words: animal feeding, antinutritional factors, legumes, neglected crops, NIRS, plant breeding. ResumenAplicación de la espectroscopía de reflectancia en el infrarrojo cercano para la estimación de los contenidos de proteína y L-canavanina en semillas de algarroba (Vicia articulata Hornem.)En este trabajo se ha evaluado la eficacia de la espectroscopía de reflectancia en el infrarrojo cercano (NIRS) en la cuantificación de dos componentes que afectan a la calidad nutricional de las semillas de algarroba (Vicia articulata Hornem.): proteína y L-canavanina, un aminoácido tóxico no proteico. Las calibraciones NIRS mostraron unos buenos estadísticos, con coeficientes de determinación múltiple (R 2 ) de 0,97 para la proteína total y 0,95 para la L-canavanina. Las ecuaciones desarrolladas fueron posteriormente utilizadas para estimar los contenidos en proteína y L-canavanina de un grupo de muestras desconocidas (validación externa). La ecuación para proteína total fue capaz de predecir con una exactitud similar a la del método de referencia, mostrando una correlación (r 2 ) entre los valores de referencia y los predichos de 0,95. En el caso de la L-canavanina, r 2 fue sólo de 0,72 y la ecuación sólo sirvió para discriminar las muestras en grupos de bajo, medio y alto contenidos.Palabras clave adicionales: alimentación animal, cultivos abandonados, factores antinutricionales, leguminosas, mejora genética vegetal, NIRS.

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Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics

Cited by 94 publications

References 15 publications

Endemic Plants as Browse Crops in Agricultural Landscapes of New Zealand

Endemic Plants as Browse Crops in Agricultural Landscapes of New Zealand

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

Application of near-infrared reflectance spectroscopy for the estimation of protein and L-canavanine contents in seeds of one-flowered vetch (Vicia articulata Hornem.)

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