On-line Vis-Nir sensor determination of soil variations of sodium, potassium and magnesium

Among proximal measurement methods, visible and near infrared (Vis-Nir) spectroscopy probably has the greatest potential for determining the physico-chemical properties of different natural resources, including soils. This study was conducted to determine the sodium, potassium and magnesium variations in a 10. Ha field located in Karacabey district (Bursa Province, Turkey) using an on-line Vis-Nir sensor. A total of 92 soil samples were collected from the field. The performance and accuracy of the Na, K and Mg calibration models was evaluated in cross-validation and independent validation. Three categories of maps were developed: 1) reference laboratory analyses maps based on 92 points 2) Full-data point maps based on all 6486 on-line points Vis-Nir predicted in 2013 and 3) full- data point maps based on all 2496 on-line points Vis-Nir predicted in 2015. Results showed that the prediction performance in the validation set was successful, with average R2 values of 0.82 for Na, 0.70 for K, and 0.79 for Mg, average root mean square error of prediction (RMSEP) values of 0.02% (Na), 0.20% (K), and 1.32% (Mg) and average residual prediction deviation (RPD) values of 2.13 (Na), 0.97 (K), and 2.20 (Mg). On-line field measurement was also proven to be successful with validation results showing average R2 values of 0.78 (Na), 0.64 (K), and 0.60 (Mg), average RMSEP values of 0.04% (Na), 0.13% (K), and 2.19% (Mg) and average RPD values of 1.57 (Na) 1.68 (K) and 1.56 (Mg). Based on 3297 points, maps of Na, K and Mg were produced after N, P, K and organic fertilizer applications, and these maps were then compared to the corresponding maps from the previous year. The comparison showed a variation in soil properties that was attributed to the variable rate of fertilization implemented in the preceding year.

[1]  Abdul Mounem Mouazen,et al.  Potential of On-Line Visible and Near Infrared Spectroscopy for Measurement of pH for Deriving Variable Rate Lime Recommendations , 2013, Sensors.

[2]  C. Hurburgh,et al.  Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties , 2001 .

[3]  H. H. Madden.  Comments on the Savitzky-Golay convolution method for least-squares-fit smoothing and differentiation of digital data , 1976 .

[4]  T. Jarmer,et al.  Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study , 2003, Plant and Soil.

[5]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[6]  R. V. Rossel,et al.  Visible and near infrared spectroscopy in soil science , 2010 .

[7]  P. Miller,et al.  Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana , 2005 .

[8]  Charlie Chen,et al.  Digitally mapping the information content of visible–near infrared spectra of surficial Australian soils , 2011 .

[9]  N. Fageria,et al.  Efficiency of Magnesium Use by Common Bean Varieties Regarding Yield, Physiological Components, and Nutritional Status of Plants , 2015 .

[10]  H. Ramon,et al.  Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy , 2010 .

[11]  Vis/Near- and Mid- Infrared Spectroscopy for Predicting Soil N and C at a Farm Scale , 2012 .

[12]  Sakae Shibusawa,et al.  Using a mobile real-time soil visible-near infrared sensor for high resolution soil property mapping , 2013 .

[13]  David J. Chittleborough,et al.  Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties , 2011 .

[14]  D. Cozzolino,et al.  The potential of near-infrared reflectance spectroscopy to analyse soil chemical and physical characteristics , 2003, The Journal of Agricultural Science.

[15]  Andrew Rawson,et al.  The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis , 2009 .

[16]  H. Marschner,et al.  Partitioning of shoot and root dry matter and carbohydrates in bean plants suffering from phosphorus, potassium and magnesium deficiency , 1994 .

[17]  H. Ramon,et al.  Effect of Wavelength Range on the Measurement Accuracy of Some Selected Soil Constituents Using Visual-Near Infrared Spectroscopy , 2006 .

[18]  J. M. Soriano-Disla,et al.  The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties , 2014 .

[19]  G. Metternicht,et al.  Testing the performance of spatial interpolation techniques for mapping soil properties , 2006 .

[20]  R. V. Rossel,et al.  Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy , 2006 .

[21]  H. Ramon,et al.  On-line measurement of some selected soil properties using a VIS–NIR sensor , 2007 .

[22]  Vincent Baeten,et al.  Multivariate Calibration and Chemometrics for near Infrared Spectroscopy: Which Method? , 2000 .

[23]  H. S. Mahmood,et al.  Sensing soil properties in the laboratory, in situ, and on-Line: A review , 2012 .

[24]  A. M. Mouazen,et al.  Data fusion techniques for delineation of site-specific management zones in a field in UK , 2015, Precision Agriculture.