Published October 19, 2012
Nutrient Management & Soil & Plant Analysis
Predicting Soil Phosphorus-Related Properties Using Near-Infrared Reflectance Spectroscopy Dalel Abdi
Dep. of Soils and Agri-Food Engineering Université Laval Québec, QC Canada G1K 7P4
Gaëtan F. Tremblay* Noura Ziadi Gilles Bélanger
Agriculture and Agri-Food Canada Soils and Crops Research and Development Centre 2560 Hochelaga Boulevard Québec, QC Canada G1V 2J3
Léon-Étienne Parent
Dep. of Soils and Agri-Food Engineering Université Laval Québec, QC Canada G1K 7P4
Near-infrared reflectance spectroscopy (NIRS) is a rapid, inexpensive, and accurate analysis technique for a wide variety of materials, and it is increasingly used in soil science. The objectives of our study were to examine the potential of NIRS to predict (i) soil P extracted by two methods [Mehlich 3 (M3P) and water (Cp)], soil total P (TP), annual crop P-uptake, and annual P-budget, and (ii) other soil chemical properties [total C (TC), total N (TN), pH, and K, Al, Fe, Ca, Mg, Mn, Cu, and Zn extracted by Mehlich 3]. Soil samples (n = 448) were taken over a 7-yr period from an experimental site in Lévis (Québec, Canada) where timothy (Phleum pratense L.) was grown under four combinations of P and N fertilizer. The NIRS equations were developed using 80% of the samples for calibration and 20% for validation. The predictive ability of NIRS was evaluated using the coefficient of determination of validation (Rv2) and the ratio of standard error of prediction to standard deviation (RPD). Results show that M3P, Cp, crop annual P-uptake, and annual P-budget were not accurately predicted by NIRS (Rv2 < 0.70 and RPD < 1.75). Similar results were found for K and Cu. However, NIRS predictions were moderately useful for TP, TN, Fe, and Zn (0.70 ≤ Rv2 < 0.80 and 1.75 ≤ RPD < 2.25), moderately successful for TC and Al (0.80 ≤ Rv2 < 0.90 and 2.25 ≤ RPD < 3.00), successful for pH and Mg (0.90 ≤ Rv2 ≤ 0.95 and 3.00 ≤ RPD ≤ 4.00), and excellent for Ca and Mn (Rv2 > 0.95 and RPD > 4.00). The NIRS predictive ability of several soil properties appears to be related to their relationship with soil organic C. Although NIRS can predict several soil properties, prediction of total P was the only soil P-related property, correlated to soil C, that was moderately useful. Abbreviations: b, slope of linear regression; Cp, P extracted in water; CV, coefficient of variation; DM, dry matter; ICP, inductively coupled plasma; M3, Mehlich 3; M3P_Col, soil P content extracted using the Mehlich 3 method and analyzed by colorimetry; M3P_ICP, soil P content extracted using the Mehlich 3 method and analyzed by ICP; N, total number of samples; NIRS, near-infrared reflectance spectroscopy; PLSR, partial least squares regression method; Rc2, coefficient of determination of calibration; Rv2, coefficient of determination of validation; Rep File, repeatability file; RPD, ratio of standard error of prediction to standard deviation; SD, standard deviation; SEC, standard error of calibration; SECV, standard error of cross-validation; SEP, standard error of prediction; SNVD, standard normal variate and detrending; TC, total carbon; TN, total nitrogen; TP, total phosphorus; 1-VR, coefficient of determination of cross-validation.
P
hosphorus is an essential nutrient and one of the most limiting for crop production. Mineral and organic P fertilizers are often applied to agricultural soils to achieve optimal crop yield, but amounts exceeding crop requirements can have a negative environmental effect. Several methods and/or techniques of soil analysis, including chemical extraction methods, have been developed to estimate the quantity of plant-available P in soils. Current soil P extraction methods, such as Mehlich 3 (Mehlich, 1984), Olsen (Olsen et al., 1954), Bray 1, Bray 2 (Bray and Kurtz, 1945), and water (Morel et al., 2000), are expensive, destructive, and both time and space consuming. The recommended Mehlich 3 method for a large range of soil types Soil Sci. Soc. Am. J. 76:2318–2326 doi:10.2136/sssaj2012.0155 Received 9 May 2012. *Corresponding author (
[email protected]) © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.
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(Ziadi and Tran, 2007) requires five chemical reagents (acetic acid, ammonium fluoride, ammonium nitrate, nitric acid, and ethylenediaminetetraacetic acid). Near-infrared reflectance spectroscopy (NIRS) is a cost-effective, time-saving, nondestructive, and environmentally sound technique that can predict many constituents from the single spectrum of a soil sample (Coûteaux et al., 2003; Viscarra Rossel et al., 2006), including P (Chang et al., 2001; Ludwig et al., 2002; McCarty and Reeves, 2006). Combined with minimal conventional reference methods, NIRS provides a good alternative to routine soil analysis (Nduwamungu et al., 2009a) with at least an 80% reduction in chemical use and laboratory costs (Foley et al., 1998). The NIRS measures the radiation absorbed by various bonds of C-H, C-C, C-N, N-H, and O-H found in organic constituents resulting in bending, twisting, stretching, or scissoring (Miller, 2001). Diffusely reflected near-infrared radiation is then correlated to measured material properties using various multivariate calibration techniques (Martens and Naes, 2001; Mouazen et al., 2010). Successful NIRS predictions have been reported for soil organic matter and texture (Ben-Dor and Banin, 1995; Fidêncio et al., 2002; Coûteaux et al., 2003; Viscarra Rossel et al., 2006; Stenberg, 2010) and for other soil properties including pH, CEC, N, P, K, Al, Fe, Ca, and Zn (Reeves et al., 1999; Chang et al., 2001; Nduwamungu et al., 2009a). Although some results appear promising, most studies use a limited number of samples (Malley et al., 2004; Nduwamungu et al. (2009a, 2009b, 2009c). Nduwamungu et al. (2009b) report moderately useful NIRS predictions for Mehlich 3 extractable Ca, Cu, and Mg and less reliable predictions for Al, Fe, K, Mn, P, and Zn. They conclude that further studies should incorporate larger sample sizes and more diverse soils. To our knowledge, NIRS was used to estimate water soluble P and total P (Bogrekci and Lee, 2005) but it has never been used to estimate P extracted in water (Cp) according to the method of Morel et al. (2000). Extracted soil P is often correlated with plant growth and P-uptake under controlled conditions (Simard et al., 1991; Tran et al., 1992) and from field studies (Ziadi et al., 2001; Messiga et al., 2010). Predicting crop P-uptake and P-budget from soil spectra would eliminate the need to establish relationships between soil test P and crop response to P fertilization. Börjesson et al. (1999), Terhoeven-Urselmans et al. (2008), and more recently St. Luce et al. (2012) link NIRS soil spectra to winter cereal N-uptake and report good predictions (Rv2 ≥ 0.70), but to our knowledge, the prediction of crop P-uptake and annual P-budget by NIRS has not been documented. The objective of this study was to evaluate the potential of NIRS to predict soil P-related properties (total soil P, soil P extracted using a Mehlich 3 solution or water, annual crop P-uptake, and annual P-budget) and other soil properties (pH, TC, TN, K, Al, Fe, Ca, Mg, Mn, Cu, and Zn).
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MATERIALS AND METHODS Experimental Site Description
Detailed information on the experimental site is provided by Bélanger and Ziadi (2008) and Bélanger et al. (2008). Briefly, the experiment was conducted between 1998 and 2007 on a gravely sandy loam soil of the Saint-André series located at the Agriculture and Agri-Food Canada research farm at Lévis, QC, Canada (46°47′ N, 71°07′ W, elevation 65 m). The experimental design was a split plot with four P treatments (0, 15, 30, and 45 kg P ha-1) as main plots and four N treatments (0, 60, 120, and 180 kg N ha-1) as subplots. The experiment had four replicates with a total of 64 subplots of equal size (1.5 by 2.1 m). Nitrogen (calcic ammonium nitrate) and P (triple superphosphate) fertilizers were applied each year, during the first week of May from 1999 to 2006, before the start of timothy growth. Potassium (KCl; 84 kg K ha-1) was applied with N and P to satisfy crop requirements. The soil Mehlich 3 P content was 35.2 mg P kg–1 when the experiment was initiated in 1998.
Soil and Plant Analyses Soil from plots (n = 64) was sampled to a depth of 15 cm in the spring before N and P was applied, each year from 2001 to 2007. Each sample consisted of 3 to 4 soil cores (2.5-cm diam.) taken randomly within the experimental plot. The composite samples were carefully mixed on site, air-dried, and gently crumbled by hand to pass through a 2-mm sieve. Soil P available to plants was characterized in the whole sample set (n = 448, Set 1) by using two methods: water extraction to determine the concentration (Cp, mg P L-1) of P ions in solution (Morel et al., 2000; Messiga et al., 2010) and the Mehlich 3 extraction (Mehlich, 1984). To determine Cp, 2 g of air-dried soil were mixed with 20 mL of distilled water and 200 μL of toluene to inhibit microbial activity. The solution was gently shaken for 16 h on a horizontal roller shaker (40 cycles min-1) before passing through a disposable cellulose acetate filter with a 0.2-μm cutoff (Minisart, Sartorius Gottingen, Germany). For the Mehlich 3 extraction, 2.5 g of air-dried soil was mixed with 25 mL of a Mehlich 3 solution (0.25 M NH4NO3 + 0.015 M NH4F + 0.001 M EDTA + 0.2 M CH3COOH + 0.013 M HNO3 buffered at pH 2.3), shaken for 5 min, and then filtered through Whatman no. 42 paper. Total soil P concentration was determined in 192 samples (collected in 2001, 2003, and 2006; Set 2, Table 1) using a method adapted from Nelson (1987) and used by Messiga et al. (2012). Briefly, 0.1 g of finely ground soil (0.2 mm) was mixed in a 50-mL boiling flask with 0.5 g K2S2O8 and 10 mL of 0.9 M H2SO4 and digested at 121°C in an autoclave for 90 min. Following Mehlich 3, water, and total P extractions, P was quantified by the colorimetric blue method (Murphy and Riley, 1962). Also, Mehlich 3 P (M3P) was measured by the inductively coupled plasma (ICP) emission spectroscopy (M3P_ ICP) in 192 samples collected in 2005, 2006, and 2007 (Set 3, Table 1). Soil pH was measured in distilled water with a 1:2 soil/ solution ratio (Hendershot et al., 1993). Total C and TN were 2319
quantified by dry combustion with a LECO CNS-1000 analyzer (LECO Corp., St. Joseph, MI). The concentrations of K, Al, Fe, Ca, Mg, Mn, Cu, and Zn were measured by ICP emission spectroscopy after the Mehlich 3 extraction (Mehlich, 1984). Potassium, Al, and Fe were analyzed from samples collected in 2005, 2006, and 2007 (n = 192, set 3), whereas Ca, Mg, Mn, Cu, and Zn were determined from samples collected in 2005 and 2007 (n = 128, set 4, Table 1). One chemical determination of each soil property was done on soil samples. From 2001 to 2006, timothy was harvested twice a year (n = 378, Set 5); the first harvest was in mid-June, at the late-heading stage of development, and the second harvest in early August. Dry matter (DM) yield of each plot was determined from strips (0.91 m wide by 2.1 m long) harvested at a 5-cm height using a self-propelled flail forage harvester (Carter MGF Co. Inc., Brookston, IN). A forage sample of approximately 500 g was collected from each plot, dried at 55°C in a forced-draft oven for 3 d, and ground with a Wiley mill (Standard model 3, Arthur H. Thomas Co., Philadelphia, PA) fitted with a 1-mm screen. Plant samples of 0.1 g were digested using a mixture of sulfuric and selenious acids, as described by Isaac and Johnson (1976). Phosphorus concentration was measured with a QuikChem 8000 Lachat autoanalyzer (Lachat Instruments) using the Lachat method 13–115–01–2-A (Lachat Instruments, 2011). The P-uptake at each harvest was calculated as the product of forage P concentration and DM yield. Annual DM yield and crop P-uptake were the sum of their first and second harvest values. The annual P-budget was computed as the dif-
ference between P applied as fertilizer and annual P-uptake, as reported by Messiga et al. (2012).
Near-Infrared Reflectance Spectroscopy Spectrum Acquisition Each soil sample was mixed and scanned by measuring its absorbance [log (1/R), where R is reflectance] in the visible and near-infrared regions between 400 and 2500 nm at 2-nm intervals using a NIRSystems 6500 monochromator Instrument (Foss NIRSystems Inc., Silver Spring, MD) with a cup (quarter cup, rectangular 1/4) containing approximately 25 mL of the soil sample. This NIRS instrument is equipped with a tungsten halogen light source, a silicon detector for wavelength between 400 and 1100, a Pbs (Lead (II) Sulfide) detector for wavelength in the range of 1100 to 2500 nm, and two intern standards (polystyrene and didymium) that are used during sample spectrum acquisition. Each spectrum was the mean of 16 coadded scans. A check test was performed before scanning the soil sample and a performance test was done daily. One randomly selected soil sample was scanned 12 times to create a repeatability file that was used to account for possible operator errors and to improve calibration equations by minimizing errors associated with soil heterogeneity and compaction (Nie et al., 2009).
Pretreatment, Calibration, and Cross-Validation To improve the calibration models of each property, the following 40 spectral pretreatments (2 × 2 × 2 × 5 factorial arrangement) were tested using WinISI III (ver.1.61) software (Infrasoft International, LLC, Silver Spring, MD): (critical T-outlier val-
Table 1. Descriptive statistics for the soil P-related and other properties analyzed using reference methods.† Property
Sampled years
N (Set no.)
Reference method
M3P_Col M3P_ICP TP
2001–2007 2005–2007 2001+ 2003 + 2006
448 (1) 192 (3) 192 (2)
Colorimetry ICP Colorimetry
Cp
2001–2007
448 (1)
Colorimetry
P-uptake P-budget
2001–2006 2001–2006
378 (5) 378 (5)
Colorimetry
TC TN
2001+ 2003 + 2006 2001+ 2003 + 2006
192 (2) 192 (2)
Dry combustion Dry combustion
pH
2001+ 2003 + 2006
192 (2)
Water (1:2)
Min
Max
Mean
SD
CV
––––––––––––––––mg kg-1––––––––––––––– 4 102 40 19 17 124 54 24 450 1242 729 136 ––––––––––––––––mg L-1––––––––––––––––
% 48 44 19
0.06 1.28 0.33 0.20 ––––––––––––––––kg ha-1–––––––––––––––– 1.6 27.7 13.5 5.9 -26.5 42.4 9 17 ––––––––––––––––g kg-1–––––––––––––––– 20.2 31.3 25.2 2.2 1.8 2.8 2.2 0.2
61 44 189 9 9
4.6 6.4 5.5 0.4 7 ––––––––––––––––mg kg-1––––––––––––––– K 2005–2007 192 (3) ICP 46 332 133 61 46 Al 2005–2007 192 (3) ICP 769 1225 995 95 10 Fe 2005–2007 192 (3) ICP 162 315 233 30 13 Ca 2005 + 2007 128 (4) ICP 986 2314 1559 271 17 Mg 2005 + 2007 128 (4) ICP 134 449 280 81 29 Mn 2005 + 2007 128 (4) ICP 13 110 36 23 64 Cu 2005 + 2007 128 (4) ICP 1.1 2.6 1.6 0.3 19 Zn 2005 + 2007 128 (4) ICP 0.8 4.2 2 0.6 30 † N, number of samples; Min, minimum; Max, maximum; SD, standard deviation; CV, coefficient of variation [(SD/mean) × 100]; ICP, inductively coupled plasma. 2320
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ues of 2.0 and 2.5) × (with and without a repeatability file) × (400–2500 nm and 1100–2500 nm wavelength section) × (14-4-1, 2-8-6-1, 2-10-10-1, 0-4-4-1, and 0-8-6-1). Low and high limits of the critical T-statistic, for T-outlier detection, were set to 2.0 and 2.5, respectively. The math treatments that were compared are identified with four numbers (i.e., 1-4-4-1); the first number is the derivative order, the second is the size of the gap in nm, the third is the number of smoothing points, and the last is the second smooth (Ludwig et al., 2002; Coûteaux et al., 2003). For each property, two criteria were used to select the best of the 40 spectral pretreatments: simultaneous low standard error (SE) and high coefficient of determination in cross-validation (1-VR) (Nduwamungu et al., 2009a). The spectral pretreatments selected for each soil property are listed in Tables 2 and 3, and only the results using the best prespectral treatments are presented. Scatter correction with standard normal variate and detrending (SNVD) was used to remove, or reduce, particle size and noise effects (Brunet et al., 2007). The modified partial least squares regression method (PLSR) of the WinISI III software was used to develop calibration equations for the soil and crop properties. To maximize the probability of developing a robust calibration equation for each property, a maximal number of soil samples, corresponding to 80% of each soil sample set, were randomly selected by the software to be used for the calibration set, and the remainder samples were used for the validation set (Ludwig et al., 2002; Brunet et al., 2007; St. Luce et al., 2012). General
calibration equations were selected based on Martens and Naes (2001) as follows: Reference data = f (spectral data) + SEC, where f () means “function of ” and SEC is the standard error of calibration. The best NIRS calibration equations were the ones that minimize the SEC. Cross-validation was performed by using four subgroups from the calibration set to choose the optimal number of terms and to avoid overfitting the calibration model (Shenk and Westerhaus, 1991).
Validation Calibration equations were validated using WinISI III software by comparing predicted against reference values. Predicted values were generated using the modified PLSR method of the WinISI III software according to Martens and Naes (2001): Predicted values = f (spectrum data) = reference data + error. The accuracy of NIRS predictions was assessed with the following statistics: the coefficient of determination of validation (Rv2) and the ratio of standard error of prediction to standard deviation (RPD), which is the standard deviation of samples in the validation set (SD) divided by the standard error of prediction corrected for the bias (SEP(C)) [RPD = SD/SEP(C)]. Calibration equations were considered to be excellent when Rv2 > 0.95 and RPD > 4.00; successful when 0.90 ≤ Rv2 ≤ 0.95 and 3.00 ≤ RPD ≤ 4.00; moderately successful when 0.80 ≤ Rv2 < 0.90 and 2.25 ≤ RPD < 3.00; moderately useful when 0.70 ≤ Rv2 < 0.80 and 1.75 ≤ RPD < 2.25; and less reliable when Rv2 < 0.70 and RPD < 1.75
Table 2. Near-infrared reflectance spectroscopy spectral pretreatments and statistics of calibration, cross-validation, and validation for the P-related soil properties.† Statistic
M3P_Col
M3P_ICP
–––––mg kg-1‡––––– Pretreatment Math treat T Region, nm Rep File Calibration Nc T-outliers Mean SEC CV, % Rc2 Cross-validation SECV 1-VR Validation Nv Mean SD
Cp
TP
––mg L-1‡–
––mg kg-1‡––
Annual P-uptake
Annual P-budget
–––––––––––––––kg P ha-1‡–––––––––––––––
2,10,10,1 2.5 400–2500 Yes
0,8,6,1 2.5 1100–2500 No
1,4,4,1 2.5 400–2500 Yes
0,4,4,1 2.5 1100–2500 No
1,4,4,1 2.5 1100–2500 No
0,8,6,1 2.5 400–2500 No
350 8 40 16 40 0.43
151 3 54 21 39 0.23
338 20 0.30 0.12 40 0.46
153 1 721 61 8 0.78
295 7 13.7 3.1 23 0.70
298 4 8.9 13.2 148 0.40
16 0.30
22 0.17
0.12 0.43
63 0.76
3.9 0.55
14.4 0.29
90 38.0 18.1
38 54.3 24.6
90 0.30 0.17
38 762 156.1
76 13.1 5.8
76 9.3 15.7
SEP(C) 15.7 21.4 0.13 78.8 4.2 14.8 † Math treat, mathematical treatment; T, critical outlier value; Region, wavelength region of the spectrum that was used; Rep File, repeatability file; Nc, number of samples used for calibration; T-outliers, outliers eliminated during calibration; SEC, standard error of calibration; CV, coefficient of variation defined as the ratio of SEC to the mean; Rc2, coefficient of determination of calibration; SECV, standard error of cross-validation; 1-VR, coefficient of determination of cross-validation; Nv, number of samples used for validation; SD, standard deviation; SEP(C), standard error of prediction corrected for the bias. ‡ The units apply only to means, SEC, SECV, SD, and SEP(C). www.soils.org/publications/sssaj
2321
(Malley et al., 2004). The coefficient of variation (CV) in the reference set was defined as the SD divided by the mean of chemical values, whereas the coefficient of variation in the calibration set was computed as the ratio of standard error of calibration (SEC) to the mean of calibration data (Williams, 2001). Predictive graphs, illustrating the relationships between predicted and reference values for P-related properties, were created with SigmaPlot for Windows (SYSTAT, 2012, version 12.1). Pearson correlations between soil total C and soil and plant properties analyzed on samples collected in 2001, 2003, and 2006 [soil P content extracted using the Mehlich 3 method and analyzed by colorimetry (M3P_Col), M3P_ICP, Cp, TP, P-uptake, P-budget, TN, and pH] were computed with the SPSS 19 software (SPSS, 2010).
pH, Al, Fe, Ca, and Cu were low (50%, Table 1) for Cp, annual P-budget, and Mn and intermediate (20–50%) for M3P_Col, M3P_ICP, annual P-uptake, K, Mg, and Zn. The CV values for the measured TP, TC, TN,
Table 3. Near-infrared reflectance spectroscopy spectral pretreatment and statistics of calibration, cross-validation, and validation for the other soil properties.† Statistic
TC
TN
pH
K
––––––––g kg-1‡––––––––
Al
Fe
Ca
Mg
Mn
Cu
Zn
–––––––––––––––––––––––––––––––––––––––mg kg-1‡ ––––––––––––––––––––––––––––––––––––––
Pretreatment Math treat
2,10,10,1
2,8,6,1
2,10,10,1
1,4,4,1
1,4,4,1
2,8,6,1
1,4,4,1
2,8,6,1
2,8,6,1
0,8,6,1
0,4,4,1
T
2.5
2.5
2.5
2.5
2
2
2
2
2.5
2
2
Region, nm
1100–2500 1100–2500 400–2500
400–2500
400–2500
1100–2500 1100–2500 400–2500
400–2500
400–2500
400–2500
Rep File
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Calibration Nc
148
153
151
150
140
141
94
93
97
91
93
T-outliers
6
1
3
4
14
13
8
9
5
11
9
Mean
25.3
2.2
5.5
129
997
232
1564
287
35
1.7
1.9
SEC
0.5
0.1
0.1
25
31
13
33
13
3
0.1
0.2
CV, %
1.9
4.5
1.8
19.4
3.1
5.6
2.1
4.5
8.5
5.8
10.5
Rc2
0.93
0.79
0.94
0.79
0.89
0.81
0.98
0.97
0.98
0.84
0.83
Cross-validation SECV
0.7
0.1
0.1
36
43
16
62
18
5
0.1
0.2
1-VR
0.88
0.73
0.89
0.58
0.80
0.70
0.94
0.94
0.95
0.79
0.78
Validation Nv
38
38
38
38
38
38
25
25
25
25
25
Mean
24.9
2.2
5.4
147
989
231
1487
260
38
1.5
2.0
SD
2.23
0.22
0.42
71
81
29
285
83
23
0.22
0.52
SEP(C)
0.79
0.10
0.13
44.6
31.6
14.1
56.3
20.5
4.1
0.19
0.25
Rv2
0.87
0.79
0.91
0.62
0.85
0.77
0.96
0.94
0.97
0.37
0.78
RPD
2.82
2.20
3.23
1.59
2.56
2.05
5.06
4.04
5.60
1.16
2.08
Prediction§
MS
MU
S
LR
MS
MU
E
S
E
LR
MU
† Math treat, mathematical treatment; T, critical outlier value; Rep File, repeatability file; Nc, number of samples used for calibration; T-outliers, outliers eliminated during calibration; SEC, standard error of calibration; CV, coefficient of variation defined as the ratio of SEC to the mean multiplied by 100; Rc2, coefficient of determination of calibration; SECV, standard error of cross-validation; 1-VR, coefficient of determination of cross-validation, Nv, number of samples used for validation, SD, standard deviation; SEP(C), standard error of prediction corrected for the bias; Rv2, coefficient of determination of validation; RPD, ratio of standard error of prediction to standard deviation which is the SD of samples in the validation set divided by the SEP(C). ‡ The units apply only to means, SD, SEC, SECV, SD, and SEP(C). § Based on validation statistics, the near-infrared reflectance spectroscopy predictions were considered excellent (E) when Rv2 > 0.95 and RPD > 4; successful (S) when 0.90 ≤ Rv2 ≤ 0.95 and 3 ≤ RPD ≤ 4; moderately successful (MS) when 0.80 ≤ Rv2 < 0.90 and 2.25 ≤ RPD < 3; moderately useful (MU) when 0.70 ≤ Rv2 < 0.80 and 1.75 ≤ RPD < 2.25; and less reliable (LR) when Rv2 < 0.70 and RPD < 1.75 (Malley et al., 2004). 2322
Soil Science Society of America Journal
Near-Infrared Reflectance Spectroscopy Prediction of Soil and Crop P Properties Statistics for calibration and cross-validation are listed in Table 2. The number of T-outliers was 20 for Cp and less than 8 for the other P properties, indicating that the development of calibration equations was based on more than 94% of the soil samples in the calibration set. Based on the high standard error and CV (>23%) and the low coefficients of determination of calibration equations (Rc2 ≤ 0.70) for M3P_Col, M3P_ICP, Cp, P-uptake, and P-budget, NIRS calibration performances were considered poor for these properties. As a result, cross-validation of calibration equations showed high standard error of cross-validation (SECV) and low coefficient of determination of cross-validation (1-VR) values (£0.55). However, the calibration for TP resulted in an acceptable coefficient of determination (Rc2 = 0.78) while the cross-validation was acceptable with a 1-VR of 0.76. The number of soil samples used in the validation set varied between 38 and 90 depending on the P-related property (Table 2). Slopes of the linear regression (b) between reference and predicted values of M3P_Col, M3P_ICP, Cp, P-uptake, and P-budget from the validation set were less than 0.60 while the Rv2 ( ≤ 0.49) and RPD values ( ≤ 1.37) were low (Fig. 1). The relationship between reference and predicted values for these properties was therefore poor [b < 0.80, Rv2 < 0.70, RPD < 1.75 (Nduwamungu et al., 2009c)], indicating that these properties cannot be predicted by NIRS. This result agrees with Nduwamungu et al. (2009b) who reports a poor calibration performance for M3P when analyzed by ICP emission spectroscopy on a limited number of soil samples (n = 150). Chang et al. (2001) also report a low accuracy for M3P_ICP prediction using NIRS with a principal component regression technique (Rv2 = 0.40, RPD = 1.18). Similarly, McCarty and Reeves (2006) found that soil P content extracted using the Mehlich 1 method and analyzed by colorimetry cannot be predicted by NIRS (Rv2 = 0.21). However, NIRS has been shown to be useful (Rv2 = 0.71; RPD = 1.81) to predict P when measured by the Olsen method (van Groenigen et al., 2003). Thus, it appears that the performance of NIRS calibration could be affected by the reference method used that might produce different reference values. Morón and Cozzolino (2007) report that NIRS predictions of soil P were slightly more reliable when based on a resin extracted P method (Rv2 = 0.61, RPD = 2.2) rather than the Bray method (Rv2 = 0.58, RPD = 1.72). Sørensen and Dalsgaard (2005) suggest that NIRS could be useful to predict soil P if there is an indirect relationship between soil P and organic components, which means that P relates to NIRS by covariation (Stenberg, 2010). Indeed, Ludwig et al. (2002) report useful calibration for soil P measured by the Olsen method, which was highly correlated with soil C content (r = 0.67). In our study, soil C content was not significantly correlated to M3P_Col (r = -0.04, P = 0.56), M3P_ICP (r = 0.10, P = 0.45), P-uptake (r = 0.08, P = 0.30), and P-budget (r = 0.02, P = 0.80), and significantly but weakly correlated to Cp (r = 0.31, P < 0.001, data not shown). Moderately useful NIRS predicwww.soils.org/publications/sssaj
tion (Rv2 = 0.78) was previously reported for water soluble P in 150 fine sandy soil samples collected from three sites in Florida (Bogrekci and Lee, 2005), but to our knowledge, the potential of NIRS to predict Cp (Morel et al., 2000), P-uptake, and P-budget has not previously been studied. Successful calibration was found for TP as indicated by high 2 Rc and 1-VR values, which resulted in a moderately useful prediction of TP from the validation set (Fig. 1) with Rv2 = 0.75 and RPD = 1.98. This result can be explained, in part, by the fact that TP contains a certain proportion of organic P that is related to organic matter (Turner et al., 2005). Bogrekci and Lee (2005) report successful NIRS prediction of TP (Rv2 = 0.92) in 150 fine sandy soil samples. Future research is needed to verify whether the ability of NIRS to predict soil TP is related to soil texture and to evaluate the potential for NIRS to predict soil organic P, since it is highly correlated with the concentration of organic matter.
Near-Infrared Reflectance Spectroscopy Prediction of Other Soil Properties Statistics of calibration and validation for all other soil properties are provided in Table 3. The number of T-outliers excluded from the calibration set was 0.95, RPD > 4.0). Furthermore, predictions were successful for Mg and moderately successful for Al. The Fe and Zn calibration equations were moderately useful, while the K and Cu calibration equations had the lowest Rv2 (
