The potential of near-infrared reflectance spectroscopy for soil analysis — a case study from the Riverine Plain of south-eastern Australia

2002 ◽  
Vol 42 (5) ◽  
pp. 607 ◽  
Author(s):  
B. W. Dunn ◽  
G. D. Batten ◽  
H. G. Beecher ◽  
S. Ciavarella

Environmental management in agricultural systems must be maintained while controlling costs and increasing productivity. To obtain a better response from inputs in agriculture, cost-effective soil analysis is needed to enable site-specific applications. Near-infrared reflectance spectroscopy (NIRS) technology has the potential to provide a rapid, low-cost analysis enabling within field variability to be identified. NIRS was evaluated for its ability to predict a range of soil properties in the Riverine Plain soils of southern New South Wales. Over 550 topsoil (0-10�cm) and 300 subsoil (40-50 cm) samples from a range of soil types were air dried and ground before scanning with a NIRSystems model 6500 scanning spectrophotometer. The Partial Least Squares (PLS) regression procedure was used to determine the best correlation (i.e. calibration) between the chemical reference data and spectral data for both topsoil and subsoil samples. A validation set of samples was used to test the predictive ability of NIRS for a number of soil properties. The results demonstrated that NIRS can successfully determine some soil properties in both the topsoil and subsoil. In the topsoil, cation exchange capacity (CEC), exchangeable Ca and Mg, pH and Ca : Mg ratio were predicted with a high level of accuracy and organic carbon and exchangeable sodium percentage (ESP) with an acceptable level of accuracy. In the subsoil, CEC, exchangeable Na, Ca, Mg, ESP, pH and Ca : Mg ratio were all predicted with a high degree of accuracy. The predictive ability of NIRS for many soil constituents may make it suitable for use in agricultural soil assessment for site-specific agriculture in the Riverine Plain soils of southern New South Wales.


Krmiva ◽  
2018 ◽  
Vol 60 (1) ◽  
pp. 9-16
Author(s):  
Robert Gąsior ◽  
Katarzyna Połtowicz

Four calibrations were made for cholesterol content in poultry meat (breasts and legs from chickens, cockerels, capons, and breasts and legs from geese). Standard uncertainties expressed as SECV (%, relative) for chickens, cockerels and capons were 9.2 for breasts and 7.8 for legs. These values for geese were 8.4 and 9.0, respectively. The discriminant method with the highest predictive ability, based on residuals RMSX Residents, was used to classify the samples. Classification accuracy values were good and ranged, on average, from 96.8% to 98%. The NIRS calibrations on cholesterol content in the breast and leg meat of chickens, capons, cockerels, as well as in the breast and leg meat of geese, are suitable for rapid routine analyses to use in practice.







2009 ◽  
Vol 89 (5) ◽  
pp. 531-541 ◽  
Author(s):  
C Nduwamungu ◽  
N Ziadi ◽  
L -É Parent ◽  
G F Tremblay ◽  
L Thuriès

Near infrared reflectance spectroscopy (NIRS) is a cost- and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r2), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.Key words: Near infrared reflectance spectroscopy, soil analysis, calibration





2009 ◽  
Vol 89 (5) ◽  
pp. 579-587 ◽  
Author(s):  
C Nduwamungu ◽  
N Ziadi ◽  
L -É Parent ◽  
G F Tremblay

Near-infrared reflectance spectroscopy (NIRS) is a cost-effective and environmentally friendly technique of soil analysis that is particularly advantageous in intensive soil sampling and soil nutrient management as well. This study evaluated the potential of NIRS for predicting P, K, Ca, Mg, Cu, Zn, Mn, Fe, and Al extracted by Mehlich 3. We used 150 air-dried samples collected from a 15-ha site dominated by Orthic Humic Gleysol and Gleyed Dystric Brunisol soils. Calibration equations were developed using modified partial least squares regression. The accuracy of NIRS prediction was evaluated using the coefficient of determination (R2), the ratio of performance deviation (RPD), and the ratio of error range (RER). Reliable calibrations were found for Ca, Cu, and Mg (R2 ≥ 0.7, RPD ≥ 1.75, and RER ≥ 8). Less-reliable calibrations were found for Al, Fe, K, Mn, P, and Zn (R2 < 0.7, RPD < 1.75, and RER < 8). In the validation with independent samples, acceptable regression coefficients (i.e., 0.8 ≤ slope ≤ 1.2) were only found for Ca, Mg, and Mn. We presumed that the pH of the Mehlich 3 extractant (2.5 ± 0.1) may affect the solubility of most of these nutrients, regardless the soil texture and, consequently, the potential of NIRS to predict them. The more a nutrient was correlated to clay content, the more it was likely predictable by NIRS. The prediction models obtained for Al, Ca, Cu, Fe, K, Mg, and Mn could still be used for screening purposes in cases where high accuracy is not required. These NIRS prediction models should be validated across larger geographic areas of geological homogeneity. Key words: Soil analysis, Mehlich 3, near-infrared reflectance spectroscopy, calibration



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