Prediction of phosphorus sorption indices and isotherm parameters in agricultural soils using mid-infrared spectroscopy

Geoderma 358 (2020) 113981

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Prediction of phosphorus sorption indices and isotherm parameters in agricultural soils using mid-infrared spectroscopy K.S. Dunnea,b, N.M. Holdenb, S.M. O'Rourkeb, A. Fenelona, K. Dalya, a b

T

⁎

Environment Soils and Land Use Department, Teagasc, Johnstown Castle, Wexford, Ireland School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Alex McBratney

Phosphorus is a macro nutrient essential for optimum crop growth and animal health. The soil’s ability to supply P in an available form is influenced by sorption capacity and P binding energies in soil. These properties are usually derived from sorption isotherms that are time consuming and difficult for routine analysis. Mid-infrared diffuse reflectance Fourier transform (MIR DRIFT) spectroscopy is a rapid analysis technique that can potentially replace extractive and digestive techniques traditionally used in soil analysis. This study explored the application of MIR DRIFT in combination with chemometrics, to predict indicators of soil fertility and quality, specifically, P sorption properties. Using an archive of 11 great soil groups a P sorption reference library was generated using five different sorption models; (1) single point sorption index; (2) Langmuir sorption isotherm in the 0–25 mg l−1 P range; (3) Langmuir sorption isotherm in the 0–50 mg l−1 P range; (4) Freundlich sorption isotherm in the 0–25 mg l−1 P range; and (5) Freundlich sorption isotherm in the 0–50 mg l−1 P range. Thirteen reference values were generated for each sample for calibration and validation of P sorption models developed from MIR DRIFT spectra. Validation of the single point P sorption index and Langmuir parameters were satisfactory for rough screening with the exception of the Langmuir binding energy in the 0–50 mg l−1 P range (k50 [8]). The P sorption capacity remaining was the best predicted parameter and the Freundlich sorption isotherm predictions were poor. The results indicated that there is potential for benchtop MIR to describe P sorption properties in agricultural soil to improve management decisions and that soil specific models could be developed to further enhance prediction performance.

Keywords: Soil Phosphorus Sorption Mid-infrared Spectroscopy Chemometrics

1. Introduction Phosphorus (P) is essential for the development of plant, animal and human cells. It is supplied to the food system via soil, but in their natural state many agricultural soils are P deficient and therefore require regular P fertilisation (McLaughlin et al., 2011). Orthophosphate is a form of P readily available to plants (Schachtman et al., 1998), it plays a role in RNA, DNA (Veneklaas et al., 2012), ATP (Smil, 2000), phospholipids (Campos et al., 2018), cell division and biosynthesis of cell membranes (Ha and Tran, 2014). When a plant is P deficient the rate of meristematic activity slows (Kavanova et al., 2006), so to maximise crop production, P must be managed by the farmer efficiently (Schoumans et al., 2015). P is naturally produced at a rate insufficient to match agricultural demand (Weaver and Wong, 2011). It is removed from the agricultural system by harvesting of plant and animal products and other accelerated losses such as soil erosion and runoff to surrounding waterbodies (Delgado and Scalenghe, 2008; van Dijk et al.,

⁎

2016). To compensate, and even increase soil P, farmers use P fertiliser, most commonly derived from rock phosphate. Food production represents around 90% of the global P demand. The extent to which P fertiliser is sorbed is dependent on the soils ability to fix P (Smil, 2000). Poorly managed, particularly excessive P application has the potential to cause eutrophication of surrounding freshwaters (Abdi et al., 2012), so control of P is important for national water quality programs (Brady and Weil, 2013) as implemented in response to the European Union Water Framework Directive (EEC, 2000). Furthermore, as rock phosphate is a non-renewable resource (Cordell et al., 2009), with studies projecting global commercial phosphate reserves being depleted in the next 50–100 years (Cordell et al., 2009), agriculture faces two specific challenges: (i) to create a sustainable P management pathway (medium to long term) by optimizing P supply, application and loss within the limits of naturally cycling P (Schoumans et al., 2015), and (ii) to optimize short term P management to allow the transformation to a sustainable footing. To improve current P management cheap,

Corresponding author. E-mail address: [email protected] (K. Daly).

https://doi.org/10.1016/j.geoderma.2019.113981 Received 20 June 2019; Received in revised form 13 September 2019; Accepted 17 September 2019 0016-7061/ © 2019 Elsevier B.V. All rights reserved.

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make this work more widely applicable, this study chose to predict a parameter that describes similar sorption mechanisms, but does not use Colwell P. Daly et al. (2017) used a single point P sorption index (PSI) to examine P dynamics of poorly drained soils in Ireland. Unlike PBI, the calculation for the PSI does not use an estimate of available P and is expressed as (Eq. (2)):

reproducible methods of measuring P status in soil, compatible with digital agriculture (Holden et al., 2018) need to be developed. This research further (Forrester et al., 2015) developed a theoretical foundation for optical sensing of P, which will provide the basis for low-cost ubiquitous sensor development and cheaper, more rapid laboratory methods for analysing soil P dynamics. Phosphorus is made available in solution through a combination of physicochemical (adsorption/desorption and precipitation/dissolution) and biological/biochemical (mineralization/immobilization) processes (Bünemann, 2015). In acidic soils the abundance and type of Al and Fe minerals is also an important regulator of the mechanisms. Sandy and neutral soils have relatively little fixation, whereas acidic, clay soils with high Fe and Al content have the highest fixation capacity. In alkaline calcareous soils, Ca is the dominant determinant of P solubility. These are some of the reasons it is important to test soil for P on a regular basis (McLaughlin et al., 2011). Current soil testing considers neither detail of P sorption and fixation processes, nor Al, Fe and Ca as controlling factors for plant available P. Optimum P management requires methods that account for soil type because P solubility and availability depend on sorption properties, which are influenced by soil type (Daly et al., 2015; Maguire et al., 2001). The ability of a soil to supply soluble P in proportion to demand (Burkitt et al., 2001) is known as its buffering capacity, and its ability to bind P is its sorption capacity. The buffering and sorption capacities indicate supply and availability of P in a water-soluble form (Daly et al., 2015). The phosphate anion (H2PO4−) often reacts with iron and aluminium mineral surfaces, which causes different degrees of fixation (Brady and Weil, 2013). A sorption mechanism in soils with high Al or Fe, is 'inner sphere anion exchange'. An example of this mechanism is where positive charges, on a hydroxyl iron surface, are capable of fixing phosphate ions. Sorption capacity is fully satisfied when sorption maximum (Smax (mg P kg−1)) is reached (Brady and Weil, 2013). When considering this mechanism, it is also important to account for the binding energy parameter, k (l mg−1), which is a constant relating to the strength with which the phosphate ion is bound (Daly et al., 2015). Phosphorus sorption includes fast reversible and slow almost irreversible chemical reactions at the molecular scale (Barrow, 1983; Schoumans and Groenendijk, 2000), therefore we believe that optical sensors could be developed to predict properties associated with sorption or parameters used to characterise P behaviour in soils. Infrared spectroscopy combined with multivariate analytical methods can be used to predict many soil properties quite accurately (Soriano-Disla et al., 2014). While it may not be quite as accurate as traditional chemical methods, the need for little sample preparation, rapid analysis, and easy replication permit high resolution field sampling for laboratory analysis (O'Rourke and Holden, 2011), or on-the-go sampling in real time (Brown et al., 2006; Kodaira and Shibusawa, 2013). The method also allows other soil properties to be determined simultaneously. Mid-infrared (MIR) spectroscopy is sensitive to molecular information compared near-infrared spectroscopy (NIR), so has the potential reflect relationships between soil properties and soil chemistry (Janik et al., 1998). For this reason, we focused on MIR spectroscopy as the foundation for developing soil P sorption measurement. Forrester et al. (2015) used mid-infrared diffuse reflectance Fourier transform (MIR-DRIFT) spectroscopy and partial least squares regression (PLSR) for prediction of a phosphorus buffer index (PBI) related to Colwell P (Moody, 2007) in Australian soils. This index is referred to in Burkitt et al. (2002) as PBI+Col (Eq. (1)):

PBI =

Ps + initial ColwellP c 0.41

PSI =

X logC

(2) −1

−1

where PSI is expressed in l mg , X is P sorbed (mg kg ) and C is the final P concentration at equilibrium (mg l−1). Note that not all studies use 1000 mg P added per kg of soil. The reference method used in this study added 1500 mg P per kg of soil, according to Bache and Williams (1971). Langmuir isotherms have also been used to describe P sorption for a range of Irish grassland soils of different parent material (Daly et al., 2015) (Eq. (3)):

C 1 k+C = × S Smax Smax

(3)

where C is concentration of P after 24 h equilibration and is expressed as mg l−1, S is P sorbed and is expressed as mg kg−1, Smax is P sorption maximum (mg kg−1), and k is a binding energy constant expressed as l mg−1. The Freundlich equation is also commonly used to explore sorption in soil (Eq. (4)):

logS = logK + nlogC

(4)

where S is the total amount of P sorbed (mg kg−1) and C is the equilibrium P concentration (mg l−1), n is the binding energy constant (l mg−1) and K is the adsorption constant (mg kg−1). The objective of this work was to discover whether MIR spectroscopy could be used for the prediction of parameters that describe phosphorus sorption in soil. This was achieved using a sample of 220 soils from 11 Great Groups (Creamer et al., 2016) to create a reference library of sorption parameters and spectral signatures to model predictions of P parameters from spectral data. 2. Materials and methods 2.1. Soil archive Uppermost horizon samples (topsoil) (n = 225) were obtained from an collection of 220 soil profile samples representative of Irish soil types retained from the creation of the Irish Soil Information System (Creamer et al., 2016) and archived at Johnstown Castle, Wexford. The uppermost horizon of the sampled soils ranged from 0.03 m to 0.55 m, with a mean depth of 0.20 m. They represented 11 Great Groups but 57% were from the classes Brown Earth and Surface Water Gley. The samples had been previously treated by drying at 40 °C and sieving to 2 mm. Samples were ball-milled prior to spectral scanning resulting in a particle size of < 0.25 mm. 3. Reference data acquisition Phosphorus (P) sorption was described using five different sorption methods to generate 13 reference values: (1) single point sorption index (PSI [1]), which was also used to calculate P sorption capacity remaining (PSCr [2]) and total phosphorus sorption capacity (derived from PSI calculated as X + Mehlich-3P (Daly et al., 2001), whereby Mehlich-3 is used to estimate P already sorbed to the soil, thus, addition of this value to X represents the total amount of P sorbed on the soil relative to a final solution concentration, PSCt [3]); (2) Langmuir sorption isotherm 0 to 25 mg l−1 range, which were used to calculate sorption maximum (Smax25 [4]), binding energy (k25 [5]) and maximum buffering capacity (MBC25 [6]); (3) Langmuir sorption isotherm 0 to 50 mg l−1 range, which were used to calculate sorption maximum (Smax50 [7]), binding energy (k50 [8]) and maximum buffering capacity

(1) −1

where Ps is P sorbed (mg P kg soil) from a single addition of 1000 mg P kg−1, c is the final solution P concentration (mg P l−1), and Colwell P (used as an estimation of P already sorbed to the soil sample) has units of mg P kg−1. Values of PBI are dimensionless. Colwell P is a parameter that is not measured all over the world, therefore in order to 2

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(MBC50 [9]); (4) Freundlich sorption isotherm 0 to 25 mg l−1 range, which was used to calculate Freundlich sorption maximum, Kmax25 [10] and Freundlich binding energy, n25 [11]; and (5) Freundlich sorption isotherm 0–50 mg l−1 range, which was used to calculate Freundlich sorption maximum, Kmax50 [12] and Freundlich binding energy, n50 [13]. PSI was determined from a single-point isotherm according to Bache and Williams (1971). Samples of soil (1.00 g) were weighed into 50 ml centrifuge tubes and 1500 mg P kg−1, as KH2PO4, was added to each, at a 1:20 soil:solution ratio in 0.01 M CaCl2. The suspensions were shaken, on an end-over-end shaker at room temperature for 18 h. They were centrifuged at 850 relative centrifugal force (RCF) for 30 min and filtered through 0.45 µm syringe. The P concentration of the centrifugate was measured colorimetrically (John, 1970) and PSI was calculated from Eq. (2). Sorption parameters were derived from the linear forms of the isotherm models (Eqs. (3) and (4)). The isotherms were determined using modification of the standardised batch technique (Nair et al., 1984). Thirty ml of P solution with initial concentrations 0, 5, 10, 15, 20, 25 and 50 mg l−1 was added to 2 g soil samples, in duplicate. Soil samples were equilibrated in 50 ml centrifuge tubes on an end-over-end shaker at room temperature for 24 h. Suspensions were filtered into small plastic cups using Whatman 42 filter paper and the concentration of P in solution was measured colorimetrically (John, 1970). Adsorption data were fitted to both Langmuir and Freundlich isotherm equations (Eqs. (3) and (4)) (using either 0–25 mg l−1 or 0–50 mg l−1).

calibrations = R2 < 0.7 and RPD < 1.75. Values of 0.5 ≤ R2 ≤ 0.7 and 1.4 ≤ RPD ≤ 2, were considered satisfactory for rough screening by Couteaux et al. (2003). The ratio of performance to inter-quartile distance (RPIQ), which is based on the quartiles of the empirical reference data distribution was proposed by Bellon-Maurel et al. (2010) as a superior chemometric indicator when skewed distributions are encountered, which is common in the analysis of soil components, therefore this was also taken into account when assessing chemometric performance (O'Rourke et al., 2016). The following classes were defined from validation RPIQ: very reliable (> 0.86), reliable (0.62–0.86) and less reliable (< 0.62). Overall, multiple criteria were examined simultaneously. 4. Results 4.1. Reference data Thirteen sorption parameters were derived from five methods that describe phosphorus sorption in soil and their chemometric models are presented in Table 1. Of 225 soil samples used to derive isotherms, at least 179 samples provided a good fit to the models (R2 > 0.8). The samples that fit the model (R2 > 0.8) were used for chemometric model development. Reference data for PSI [1] ranged from −149.67 to 812.18 mg kg−1, phosphorus sorption capacity remaining (PSCr) [2] ranged from −293.40 to 1068.40 (mg kg−1) and total phosphorus sorption capacity (PSCt) [3] ranged from −158.63 to 1094.60 (mg kg−1). One hundred and seventy-nine samples fit the Langmuir model when determined using 0–25 mg l−1 sorption aliquots. Langmuir sorption maximum, Smax25 [4] ranged from 119.16 to 856.64 mg P kg−1. Langmuir binding energy, k25 [5], ranged from 0.03 to 100.76 l mg−1 and Langmuir model-derived maximum buffer capacity, MBC25 [6] ranged from 10.26 to 48226.33 l kg−1. All Smax25 and k25 values were positive except for one soil sample, which was a Humic Lithosol, with Smax25 = 119.16 and k25 = −3.58. One hundred and ninety six samples fit the Langmuir model when determined using 0–50 mg l−1 sorption aliquots. Langmuir sorption maximum, Smax50 [7] ranged from 274.39 to 1047.77 mg P kg−1, Langmuir binding energy, k50 [8], ranged from 0.03 to 45.44 l mg−1 and Langmuir model-derived maximum buffer capacity, MBC50 [9], ranged from 10.68 to 31706.70 l kg−1. One hundred and ninety-six samples fit the Freundlich model when determined using 0–25 mg l−1 range. Freundlich sorption maximum, Kmax25 [10] ranged from 3.55 to 2316.17 mg P kg−1 and Freundlich binding energy, n25 [11] ranged from 0.10 to 1.43 l mg−1. One hundred and ninety-six samples also fit the Freundlich model when determined using 0 to 50 mg l−1 sorption aliquots. Freundlich sorption maximum, Kmax50 [12] ranged from 6.35 to 858.06 mg P kg−1, and Freundlich binding energy, n50 [13] ranged from 0.20 to 1.13 l mg−1. Reference data distributions (Table 1) were examined by box and whisker plots and outliers were removed. The whiskers were the highest and lowest values that are not outside 1.5 times the interquartile range. Values outside the whiskers were referred to as outliers. The values for PSI were similar to previously reported results (Daly et al., 2015, 2017; de Campos et al., 2016; Mozaffari and Sims, 1994) indicating that the soils used captured a normal range of characteristics and the samples were reliably analysed. After Freundlich reference data outliers had been removed the values determined in the 0–25 mg l−1 range also aligned with previous work by Daly et al. (2017). The reference data outlier which occurred most (for 9 out of the 13 parameters of interest) was from a drained ombrotropic peat soil with loam to sandy loam texture. This sample had greater organic matter (OM) content (32.25%). Soil with high OM content often has poor sorption properties because phosphorus is often occluded from binding sites where OM is high, primarily due to competitive inhibition (Daly et al., 2001; Guppy et al., 2005). This soil has limited agricultural applications and would be regarded as marginal for production, so its exclusion had

3.1. Spectral data acquisition Ball-milled soil samples were scanned using a Perkin-Elmer Spectrum 400 FT-IR instrument with a DRIFT accessory. Spectra were collected in absorbance. Instrument settings were, 16 scans for each replicate, resolution was 4 cm−1, data interval was 2 and the wavenumber range was 4000 cm−1–450 cm−1. Once absorbance [log (1/R), where R is reflectance] of each sample was read in triplicate, spectra were exported as text files, averaged and modelled in RStudio. 3.2. Spectral pre-processing and model development The dataset was split randomly as 75% for calibration and 25% for validation. Levene's and t-tests were carried out on the pairs of calibration and validation sets of each predicted parameter, to test for homogeneity of variance and to test for a significant difference in means. The 'pls' package in RStudio was used for modelling. Partial Least Square Regression, which is popular for soil quantitative analysis (Soriano-Disla et al., 2014; Wold et al., 2001) was used. Both PLSR and PLSR combined with bootstrap aggregation (bagging) were evaluated with a number of pre-processing techniques, and the method that produced the best prediction for each parameter was recorded (Table 1). To avoid overfitting, using just the calibration samples, the model with the optimum number of PLSR factors was selected as the one that minimised the root mean square error (RMSE) of cross-validation. The optimum number of PLSR factors was 11 for all models. In addition nine different pre-processing treatments were tested: (1) none (i.e. raw spectra), (2) trimming, (3) trimming and Savitzky-Golay filter, (4) trim, filter and standard normal variate (SNV), (5) trim, filter and multiplicative scatter correction (MSC), (6) trim, filter and extended multiplicative scatter correction (EMSC), (7) trim, filter and baseline correction (BLC), (8) trim, filter and first derivative of the spectra and (9) trim, filter and second derivative of the spectra. The pre-processing treatment with the best prediction performance was identified by looking for simultaneous occurrence of a high coefficient of determination (R2), high ratio of performance deviation (RPD), and low root mean square error (RMSE) in the validation results. The classification of Nduwamungu et al. (2009) for NIR models was applied: very reliable calibrations = R2 > 0.9 and RPD > 3, reliable calibrations = 0.7 ≤ R2 ≤ 0.9 and 1.75 ≤ RPD ≤ 3, and less reliable 3

PSI PSI(2) PSCr PSCr(2) psct psct(3) Langmuir Smax25 Langmuir Smax25(2) Langmuir Smax25(3) Langmuir k25 Langmuir k25(2) Langmuir k25(3) MBC25 MBC25(2) Langmuir Smax50 Langmuir k50 Langmuir k50(2) Langmuir k50(3) MBC50 MBC50(2) Freundlich Kmax25 Freundlich Kmax25(2) Freundlich Kmax25(3) Freundlich n25 Freundlich n25(2) Freundlich Kmax50 Freundlich Kmax50(2) Freundlich n50 Freundlich n50(2)

[1]

4

225 218 225 218 219 213 179 171 171 179 164 164 179 169 196 196 184 184 196 185 196 185 185 196 194 196 189 196 192

n

Raw Raw Raw T + SG + 1st derivative + bag Raw Raw Raw Raw T + SG + 2st derivative + bag Raw Raw T + SG + EMSC Raw Raw Raw Raw Raw T + SG + 1st derivative + bag Raw Raw Raw Raw T + SG + 1st derivative + bag Raw Raw Raw Raw Raw T + SG + 1st derivative + bag

Preprocessing

validation

validation

validation

validation

validation

−1

−149.67 to 812.18 (l kg ) −60.69 to 565.26 (l kg−1) −293.40 to 1068.40 (mg kg−1) −16.20 to 916.80 (mg kg−1) −158.63 to 1094.60 (mg kg−1) −1.18 to 995.25 (mg kg−1) 119.16–856.64 (mg kg−1) 195.89–680.97 (mg kg−1) 195.89–680.97 (mg kg−1) −3.58 to 100.76 (l mg−1) 0.03–5.05 (l mg−1) 0.03–5.05 (l mg−1) −426.24 to 48226.33 (l kg−1) −426.24 to 2863.70 (l kg−1) 274.39–1047.77 (mg kg−1) 0.03–45.44 (l mg−1) 0.03–2.13 (l mg−1) 0.03–2.13 (l mg−1) 10.68–31706.70 (l kg−1) 10.68–1776.67 (l kg−1) 3.55–2316.17 (mg kg−1) 3.55–623.45 (mg kg−1) 3.55–623.45 (mg kg−1) 0.10–1.43 (l mg−1) 0.19–1.08 (l mg−1) 6.35–858.06 (mg kg−1) 6.35–541.19 (mg kg−1) 0.20–1.13 (l mg−1) 0.20–0.88 (l mg−1)

Range min - max (unit)

264.39 258.77 450.00 448.80 516.29 516.29 429.42 424.76 424.76 1.63 1.50 1.50 737.80 694.24 668.13 0.71 0.69 0.69 491.99 455.21 241.51 230.91 230.91 0.53 0.54 226.12 223.83 0.48 0.48

Median

142.10 119.83 211.28 185.67 208.44 187.97 114.83 97.55 97.55 8.24 1.14 1.14 3843.16 635.87 152.97 3.32 0.48 0.48 2326.45 403.87 273.10 141.22 141.22 0.21 0.19 138.73 112.58 0.15 0.14

s.d.

0.63 0.72 0.66 0.77 0.74 0.75 0.70 0.73 0.81 0.27 0.72 0.66 0.30 0.66 0.75 0.58 0.72 0.79 0.22 0.74 0.41 0.50 0.62 0.50 0.46 0.57 0.52 0.51 0.56

82.61 62.89 118.30 86.78 97.97 93.09 62.26 50.25 42.57 7.75 0.61 0.67 3517.81 367.18 78.27 0.63 0.26 0.22 2344.66 204.67 209.41 99.21 86.60 0.15 0.13 86.78 80.87 0.11 0.09

1.67 1.89 1.73 2.09 1.98 2.01 1.84 1.95 2.30 1.18 1.89 1.72 1.14 1.74 2.00 1.56 1.91 2.20 1.14 1.97 1.32 1.42 1.63 1.42 1.37 1.53 1.45 1.44 1.52

1.06 1.38 1.19 1.51 1.55 1.69 0.94 1.11 1.31 0.08 0.83 0.76 0.11 1.03 1.69 0.46 1.12 1.29 0.10 1.09 0.46 0.89 1.02 0.75 0.86 0.98 0.93 0.80 1.04

RPIQc

0.65 0.59 0.67 0.67 0.39 0.60 0.60 0.37 0.44 0.17 0.57 0.62 −0.02 0.60 0.67 −0.02 0.44 0.47 0.01 0.53 −0.02 0.19 0.25 0.34 0.27 0.04 0.49 0.39 0.22

R2v

RPDc

R2c RMSEc

Validation

Calibration

93.86 78.40 135.47 112.90 244.32 125.17 73.45 79.20 72.05 5.42 0.70 0.66 3736.67 426.86 79.71 6.48 0.34 0.33 1154.68 282.10 350.84 227.01 126.13 0.19 0.19 172.84 68.28 0.14 0.13

RMSEv

1.67 1.55 1.71 1.75 0.99 1.50 1.56 1.22 1.34 0.84 1.55 1.65 0.25 1.49 1.72 0.99 1.36 1.38 0.46 1.45 0.76 0.63 1.14 0.99 1.13 0.90 1.43 0.94 1.11

RPDv

0.95 1.08 1.07 1.38 0.61 0.86 0.76 0.78 0.86 0.09 0.62 0.66 0.09 0.46 1.55 0.03 0.46 0.47 0.23 0.85 0.32 0.38 0.69 0.58 0.51 0.54 1.40 0.56 0.37

RPIQv

PSI, phosphorus sorption index; PSCr, phosphorus sorption capacity remaining; psct, total phosphorus sorption capacity; Smax25, Langmuir sorption maximum in the 0 – 25 mg l−1 P region; k25, Langmuir binding energy in the 0–25 mg l−1 P region; MBC25, maximum buffer capacity in the 0–25 mg l−1 region; Smax50, Langmuir sorption maximum in the 0–50 mg l−1 P region; k50, Langmuir binding energy in the 0–50 mg l−1 P region; MBC50, maximum buffer capacity in the 0–50 mg l−1 region; Kmax25, Freundlich sorption maximum in the 0–25 mg l−1 P region; n25, Freundlich binding energy in the 0–25 mg l−1 P region; Kmax50, Freundlich sorption maximum in the 0–50 mg l−1 P region; n50, Freundlich binding energy in the 0 – 50 mg l−1 P region.

[13]

[12]

[11]

[10]

[9]

[7] [8]

[6]

[5]

[4]

[3]

[2]

Parameter

Text ref

Table 1 Partial least squares regression performance statistics for the prediction and independent validation of phosphorus sorption parameters using mid-infrared spectra. The parameters and associated performance statistics. Values in bold represent statistics prior to model optimisation (outlier removal and spectral pre-processing) are in bold. Samples were randomly split 75% for the calibration set and 25% for the validation set.

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little implication for the agricultural interpretation intended for the study. These confounding factors are also the reasons for negative values in sorption model ranges where slight desorption occurs. The number of samples where desorption has occurred in this study are few, but removing them for the only reason that they are negative, had an adverse impact on prediction performance parameters, therefore if these samples fit the sorption models (R2 > 0.8), they were left in for PLSR. Due to the random split of the datasets between calibration and validation, Levene's and t-tests were carried out to test for homogeneity of variance and to test for a significant difference in means. All calibration and validation sets had a homogeneity of variance (P < 0.05). One parameter, the Freundlich binding energy parameter (n50, with 196 samples) had a significant (P < 0.05) difference in means, however when 4 reference data outliers were removed, the sets then had no significant difference in means.

4.2. PSI derived parameter prediction Model performance parameters are presented in Table 1. For PSI [1], the initial prediction, which included all data (225 samples) was satisfactory for rough screening. Calibration of this parameter improved when reference data outliers were removed (R2c = 0.63–R2c = 0.72), but validation did not improve (R2v = 0.65–R2v = 0.59). The best prediction for this parameter was when the spectra were modelled without pre-processing. Spectral pre-processing improved the model for PSCr [2] at calibration (R2c increased from the status of satisfactory for rough screening, R2 = 0.66, using raw spectra, to reliable, R2 = 0.73, using spectral trimming, Savitzky-Golay filter, first derivative and with PLSR bagging, the RMSEc decreased from 118.30 to 106.54), but outlier removal combined with this spectral pre-processing had the greatest effect (R2c increased from 0.66 to 0.77 (Fig. 1), R2v remained the same (0.67) (Fig. 2) once outliers were removed, the RMSEc decreased to 86.78 and RMSEv decreased to 112.90). According to validation results, when outliers were removed, it was also possible to achieve a prediction that was satisfactory for rough screening for the PSCt [3] parameter R2v went from 0.39 to 0.60 and RMSEv went from 244.32 to 125.17.

Fig. 2. Goodness of fit plot for independent validation of the best predicted sorption parameter ('pscr/X' [2], modelled using bagging PLSR, with first derivative spectral preprocessing and reference data outliers removed).

4.3. Langmuir sorption isotherm parameter prediction When outliers were removed Smax25 [4] did not improve at validation (R2v = 0.60, R2v = 0.44) but did at calibration ((R2c = 0.70, R2c = 0.81). A PLSR model satisfactory for rough screening was created for the Langmuir derived binding energy, k25 [5] (R2c = 0.66, R2v = 0.62). Nine spectral pre-processing methods were trialled and PLSR with spectral trimming, Savitzky-Golay filter and extended multiplicative scatter correction (EMSC), proved to yield the best prediction of k25. The result prior to removal of outliers was poor (R2c = 0.27 R2v = 0.17), which can be seen in Table 1, however the result of the model where outliers were removed and prior to spectral pre-processing was satisfactory in calibration, however less reliable in validation (R2c = 0.72 R2v = 0.57). Maximum buffer capacity was calibrated to a standard satisfactory for rough screening, MBC25 [6] (R2c = 0.66, R2v = 0.60). Calibration results for Smax50 [7] (R2c = 0.75) (Fig. 3) were reliable. Validation results suggest Smax50 [7] (R2v = 0.67) (Fig. 4) (Table 1) can be calibrated to a standard satisfactory for rough screening. A model for k50 [8] could not be validated with outliers included (R2v = -0.02). When 12 reference data outliers were taken out and spectral data was pre-processed the validation R2 increased to 0.47, and the calibration R2 went from 0.58 up to 0.79. Simply removing outliers and modelling k50 using raw spectra the results were as follows; R2c = 0.72 and R2v = 0.44. Similar model results were observed between the model using raw spectra and the model using heavily preprocessed spectra or the bagging PLSR method, so long as reference data outliers were removed. Validation results suggest MBC50 [9] (R2v = 0.53) can be predicted to a standard satisfactory for rough screening. Calibration results for MBC50 [9] (R2c = 0.74) were reliable. 4.4. Freundlich sorption isotherm parameter prediction Freundlich derived sorption capacity in the 0–25 mg l−1 P isotherm range, Kmax25 [10]; R2c increased from 0.41 to 0.62 and Kmax25 R2v increased from −0.02 to 0.25 when outliers were removed and spectra were pre-processed. Model results, after outliers had been removed, and spectral pre-processing had been applied for n25 [11] (R2c = 0.46 , R2v = 0.27) were poor. Using raw spectra, Kmax50 [12] R2v increased from 0.04 to 0.49 by simply removing reference data outliers. The

Fig. 1. Goodness of fit plot for calibration of the best predicted sorption parameter ('pscr/X' [2], modelled using bagging PLSR, with first derivative spectral preprocessing and reference data outliers removed). 5

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using PLSR for Australian soils. Most of the wet chemical methods mentioned for the determination of phosphorus sorption parameters in soil, are time-consuming, with the method for the determination of Phosphorus Sorption Index (PSI (Eq. (2))) (Bache and Williams, 1971), being the least time-consuming of the 5 methods used in this study, although an isotherm method is needed if binding energy information is required. The single point sorption index, which correlates highly with the Langmuir P sorption maxima (de Campos et al., 2016; Mozaffari and Sims, 1994), has be used to predict soil P sorption capacity (Burkitt et al., 2002; Mozaffari and Sims, 1994) to overcome the lengthy nature of determining isotherms. However, it is still a chemical extraction procedure, which has its disadvantages, such as, the time it takes to complete, the production of chemical waste and the associated cost. The PSI method has been used to investigate leaching as a P transport pathway suspected to contribute to eutrophication (Andersson et al., 2013; Djodjic et al., 2004). We can see potential for this foundation work that predicts PSI and it's derived parameters by diffuse reflectance infrared (DRIFT) spectroscopy in the mid- region of the spectrum, for the development of a bench top laboratory method that is rapid compared to wet chemical analysis. We can also see potential for the prediction of PSI and its derived parameters as a field method in the digital and precision agriculture sphere where it has been stated that sampling intended to describe large areas of soil by wet chemical analysis could be inadequate, and therefore interpretation of results inaccurate (Janik et al., 1998). The potential rapidness of a field method that predicts PSI and its derived parameters, means large agricultural areas can be sampled at a greater rate, making results more accurate, leading to better advisory services, in turn saving land from over-application of fertiliser and reducing eutrophication of waterbodies associated with fertiliser run-off. Typically, lengthy batch equilibrium experiments (Nair et al., 1984) have been used to generate P sorption isotherms (Eqs. (3) and (4)), which in turn, could be used to calculate P sorption maximum and P binding energies, for soils with different properties or which have been treated in different ways (e.g. varying crop rotation, varying tillage or varying manure addition). However, while useful for agronomic and environmental characterisation of the P sorption capacity of soils, P sorption isotherms are too time-consuming and expensive (Xue et al., 2014) for routine use in commercial laboratories. However, P sorption indices are used for P management decisions and MIR predictions of these parameters is novel and shows potential for use in commercial laboratories. Without the need for pre-processing or removal of outliers, and regardless of isotherm range (0–25 mg l−1 P or 0–50 mg l−1 P), the Smax parameter had a reliable prediction at calibration (R2 from 0.70 to 0.75) and results that suggest this robust prediction is satisfactory for screening at validation (R2 ranged 0.60–0.67). This is promising for the development of a field device calibration. The Langmuir binding energy parameter in the 0–25 mg l−1 P region (k25) gave very poor results prior to removal of outliers and preprocessing, but when outliers were removed and a Savitzky-Golay filter and extended multiplicative scatter correction (EMSC) was applied the calibration became satisfactory for screening. For the Langmuir derived parameter, k50, there was not a great difference between the model using raw spectra and the model using heavily pre-processed spectra or the bagging PLSR method, so long as reference data outliers were removed. Freundlich sorption maximum (Kmax) was better predicted in the 0–50 mg l−1 P range than the 0–25 mg l−1 P range. Predictions of Freundlich affinity constants (n25 and n50), including outliers and using raw spectra, were poor and attempts at improving the model, through outlier removal, spectral pre-processing and the trial of a second regression model (bagging PLSR) gave poorer results. Freundlich derived sorption capacity (Kmax) was not as well predicted in MIR compared to Langmuir sorption max (Smax), regardless of the range (0–25 mg l−1 P or 0–50 mg l−1 P). Confounding factors in this study were chemical and physical

Fig. 3. Goodness of fit plot for calibration of sorption parameter, Smax50 [7], modelled using PLSR and raw spectra.

Fig. 4. Goodness of fit plot for independent validation of sorption parameter, Smax50 [7], modelled using PLSR and raw spectra.

results for n50 [13] calibration indicated predictions satisfactory for rough screening (R2c = 0.56), however validation results for this parameter were poor (R2v = 0.22).

5. Discussion Phosphorus sorption capacity remaining, PSCr [2], with reference data outliers removed, was the best predicted sorption parameter overall, but all three parameters associated with the single point sorption index (PSI [1], PSCr [2] PSCt [3]) were predicted to a standard that is at least satisfactory for rough screening (R2v ≥ 0.60). Forrester et al. (2015) was also successful when predicting a similar parameter that uses a single point method, Phosphorus Buffering Index (PBI (Eq. (1))), 6

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sorption mechanisms and perhaps competitive sorption phenomena. This is why one isotherm was predicted better than the other and why reference data outliers made a big difference to performance depending on whether they were taken out or left in the model. Sorption can happen at different rates and with different chemical and physical (surface) mechanisms this is why there was an attempt made to predict parameters from two different types of isotherms in this study. The Freundlich isotherm has been conceptualised as the sum of individual Langmuir isotherms (Limousin et al., 2007), therefore, the Langmuir isotherm is the more rudimentary model, hence the better prediction of the Langmuir parameters could be due to the calculation of the Langmuir reference parameters being more elementary, therefore staying true to molecular mechanisms which are detectable using mid-infrared spectroscopy. It is best practice to apply the simplest model to the data and only choose more complex models, when necessary, based on mechanistic knowledge of the system (e.g. sorption isotherm plateaus or numerous types of adsorption sites) (Limousin et al., 2007). In order to take competitive sorption phenomena into account other modified Freundlich models have been built (Limousin et al., 2007), however the one used for this work is not one which takes competitive sorption phenomena into account, therefore, these parameters were better predicted when the outliers containing organic matter were removed. These confounding factors could possibly be overcome by use of soil type specific calibrations, and to examine this further, subsets of samples described as Brown Earth (n = 86) and Surface Water Gley (n = 35) were selected to test if a soil type specific model performed better compared to the model which included all Great Groups (n = 225). These 3 sets had homogeneity of variance (P < 0.05) and according to t-tests, had no significant difference in means. One parameter was chosen for this further investigation (PSCr [2]). The Brown Earth set had higher RPD, and lower RMSE than the all soils set and of the 3 sets, the Surface Water Gley model predicted PSCr [2] best. This model had the highest RPD and the lowest RMSE (Table 2), which demonstrates that a soil type specific model has the potential to predict phosphorus sorption in soil to a greater accuracy than a generic soil model, although this should be investigated further, as there were limitations to this further test; the Surface Water Gley set had a low number of samples, only one sorption parameter was investigated and cross validation was used opposed to independent validation which was used to predict sorption parameters [1–13] using all Great Groups.

Fig. 5. Principal components analysis (PCA) plot of score 2 v. score 1 corresponding to spectra of samples with organic matter greater than 20% (°) and samples with organic matter less than 20% (•).

5.1. Spectral scores and loadings It was evident from a principal component analysis (PCA) that if one was to build on this work, a soil type specific approach could be recommended. A PCA was carried out using the mean spectrum from all samples (n = 225) and spectral features contributing to most variance in the data were identified from scores (Fig. 5) and loadings plots (Fig. 6). The scores plot (Fig. 5) shows that there is a difference in spectral features between samples with organic matter greater than Table 2 Partial least squares regression performance statistics for the prediction and cross validation of phosphorus sorption capacity remaining [2] in various sets of soil type using mid-infrared spectra. Parameter (unit)

PSCr (mg kg−1)

PSCr (mg kg−1)

PSCr (mg kg−1)

Soil type n range of cal set

Brown Earth 89 −13.20 to 914.60 447.86 200.12 9 97.42 115.60 1.73

Surface Water Gley 35 29.40–695.60

All soil types 225 −116.20 to 1068.40 450.53 205.03 4 87.69 158.90 1.29

mean of cal set sd PC var explained (%) RMSE CV RPD

421.52 167.77 6 97.99 88.29 1.90

Fig. 6. Principal components analysis (PCA) plot of spectral loadings; PC 1, PC 2 and PC 3.

20% and samples with organic matter less than 20%. These two groups are identifiable as they from two different groups on the positive and negative sides of principal component (PC) 1, with a small number of samples misclassified. The majority of samples with high organic matter (> 20%) are on the positive side of PC1 and the majority of samples with low organic matter (< 20%) are on the negative side of PC1. This separation of features can also be seen in the loadings plots (Fig. 6). In the first PC, which accounts for 67.19% of variance in the spectral data, the strong positive peaks at 2892.9 cm−1, 2857.1 cm−1 and 1821.4 – 1607.1 cm−1 mostly represent organic matter (Soriano-Disla et al., 2014) with some weaker peaks at 892.9 cm−1 and 750.0 cm−1, which 7

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indicate iron oxyhydroxides (Soriano-Disla et al., 2014) and quartz (Forrester et al., 2015) respectively. In PC 2, which accounts for 17.18% of variance in the spectral data, strong positive loadings can be identified for peaks related to clay minerals (3642.9 cm−1), quartz (1178.6 cm−1), (1107.1 cm−1) iron oxyhydroxides (821.4 cm−1 and 785.7 cm−1) and iron oxides (714.3 cm−1 and 642.9 cm−1) (SorianoDisla et al., 2014). In PC 2, strong negative loadings can be seen at 2000.0 cm−1, 1892.9 cm−1, 1857.1 cm−1 which indicate quartz (Forrester et al., 2015) and at 1714.3 cm−1 which specifically indicates carboxylic acids and 1607.1 cm−1, 1500.0 cm−1 and 1357.1 cm−1 which indicate organic matter (Soriano-Disla et al., 2014). In PC 3, which accounts for 5.25% of variance in the spectral data, there is complete separation between positive peaks that represent clay minerals (3678.6 cm−1 and 3607.1 cm−1) quartz (1142.9 cm−1 and 1000.0 cm−1) iron oxyhydroxides (892.9 cm−1 and 821.4 cm−1) and iron oxides (678.6 cm−1 and 607.1 cm−1) and negative troughs at 2928.6 cm−1, 2857.1 cm−1 and 1714.3 cm−1 that indicate the presence of organic matter (Soriano-Disla et al., 2014). Identifiable spectral features in the loadings plots are indicative of soil properties that are known to influence P dynamics in soil (de Campos et al., 2016; Guppy et al., 2005; Herlihy and McGrath, 2007; McLaughlin et al., 2011).

use. The generic models of phosphorus sorption related parameters across 11 Great Soil Groups was capable of rough screening of agricultural soils, with potential for soil specific models to further enhance prediction performance.

6. Conclusions

References

Mid-infrared diffuse reflectance Fourier transform (DRIFT) spectroscopy was used for the prediction of phosphorus sorption properties for the population of agricultural soils found in Ireland. Five different sorption methods were calibrated with MIR spectra; (1) single point sorption, (2) Langmuir sorption isotherm in a 0–25 mg l−1 P range, (3) Langmuir sorption isotherm in a 0 to 50 mg l−1 P range, (4) Freundlich sorption isotherm in a 0–25 mg l−1 P range and (5) Freundlich sorption isotherm in a 0–50 mg l−1 P range. The predictions were at best only satisfactory for rough screening of PSI, PSI derived parameters, Langmuir Smax (0–25 mg l−1 P and 0–50 mg l−1 P), the Langmuir binding energy in the 0–25 mg l−1 P range and maximum buffer capacity in the 0–25 mg l−1 P range. All single point sorption parameters (PSI [1]), (PSCr [2]) and (PSCt [3]) were predicted to a standard satisfactory for rough screening, and PSCr [2], was the best predicted sorption parameter. Regardless of isotherm range, raw spectra with outliers removed, performed better than a full data set with preprocessed spectra for predicting MBC. A satisfactory for rough screening model was created for the Langmuir derived binding energy, k25 and PLSR with spectral trimming, Savitzky-Golay filter and extended multiplicative scatter correction (EMSC), proved to yield the best prediction of k25. For k25, the result prior to removal of outliers was poor, however the result of the model where outliers were removed and prior to spectral pre-processing was satisfactory for rough screening. For the Langmuir derived parameter, k50, there was not a great difference between the model using raw spectra and the model using heavily preprocessed spectra or the bagging PLSR method, so long as reference data outliers were removed. Calibrations of Freundlich affinity constants n25 and n50 were poor and attempts at improving the model gave poorer results. Removing reference data outliers proved to be as useful as spectral pre-processing for parameters related to P sorption in soil. When specific soil types were tested, using cross validation, there was indication that a more thorough study with equal sample input and independent validation for all sorption parameters could be successful. To conclude, mid-infrared diffuse reflectance Fourier transform (DRIFT) spectroscopic prediction of P sorption parameters could be combined with local P tests to give a better understanding of a soil's phosphorus sorption capacity. This would be a useful combination of analytical techniques, because P buffering and sorption capacities can significantly influence supply and availability of P in a water-soluble form. The results arising from this work indicate that there is potential to use benchtop spectroscopy to describe P sorption properties, in agricultural soil, that can inform P management for sustainable nutrient

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was funded under the Teagasc Walsh Fellowship Fund (RMIS 6502). The authors would like to thank Mr. Denis Brennan and Ms. Maria Pettitt for laboratory analysis. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoderma.2019.113981.

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