Jean-Philippe Gras

Bernard G Barthès
email: <bernard.barthes@ird.fr>

Brigitte Mahaut

Séverine Trupin

Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils

Keywords: Soil organic matter, nutrients, calcium carbonate, visible and near infrared reflectance spectroscopy (VNIRS), proximal soil sensing

Highlights

 Field-acquired spectra allowed good VNIRS calibrations of usual soil properties.

 Spectra acquired on auger cores yielded the best results in the field.

 Prediction accuracy was similar to that achieved using 2-mm sieved dried samples.

Introduction

There is increasing concern about soil quality, its degradation and its protection, and this regards agricultural soils particularly (European Commission, 2006). As a consequence, there is increasing demand for the monitoring of agricultural soil attributes [START_REF] Huber | [END_REF].

Decision-makers and land use planners are interested in soil quality, and of course farmers too. However, practical assessment of soil quality requires the monitoring of several attributes, which may vary meaningfully over short distances thus have to be characterized at high spatial density. In such conditions, appropriate monitoring of agricultural soil attributes using conventional soil analyses is expensive and time-consuming.

Diffuse infrared reflectance spectroscopy, especially near infrared reflectance spectroscopy (NIRS) and visible and NIRS (VNIRS), has been reported to represent a relevant alternative for assessing and monitoring soil quality [START_REF] Cécillon | Assessment and monitoring of soil quality using near infrared reflectance spectroscopy (NIRS)[END_REF]. Carried out in the laboratory, this approach allows time-and cost-effective determination of numerous soil properties such as carbon and nitrogen contents, texture, pH, but also relating to organic matter quality and microbial processes, and even to the abundance of soil organisms [START_REF] Malley | Application in analysis of soils[END_REF]Barthès et al., 2006[START_REF] Barthès | Near infrared reflectance spectroscopy (NIRS) could be used for characterization of soil nematode community[END_REF].

In addition, VNIRS can also be performed directly in the field, which improves time-and cost-effectiveness (no sample packaging and preparation required), and is particularly useful when high spatial resolution is needed [START_REF] Stenberg | Visible and near infrared spectroscopy in soil science[END_REF]. However, in situ measurement of soil reflectance is influenced by conditions such as soil moisture, structure, stoniness, coarse organic residues, contamination of the probe sensor by dust, possible changes in the distance between sensor and soil, and as far as spectroradiometers are used, changes in solar illumination [START_REF] Baumgardner | Reflectance properties of soils[END_REF][START_REF] Sudduth | Soil organic-matter, CEC, and moisture sensing with a portable NIR spectrophotometer[END_REF][START_REF] Lagacherie | Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements[END_REF][START_REF] Stenberg | Visible and near infrared spectroscopy in soil science[END_REF][START_REF] Gubler | Quantitative Estimations of Soil Properties by Visible and Near Infrared Spectroscopy -Applications for Laboratory and Field Measurements[END_REF]. Though these conditions are not favorable to VNIRS characterization of soil properties, literature has mostly reported promising results using field VNIRS, especially for determining clay, organic carbon and total nitrogen contents [START_REF] Ben-Dor | A novel method of classifying soil profiles in the field using optical means[END_REF][START_REF] Stevens | Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils[END_REF][START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF][START_REF] Viscarra Rossel | In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy[END_REF][START_REF] Kusumo | Predicting soil carbon and nitrogen concentrations and pasture root densities from proximally sensed soil spectral reflectance[END_REF]; some authors nevertheless failed to achieve accurate predictions of moisture, organic and inorganic carbon contents, total nitrogen content or cation exchange capacity [START_REF] Sudduth | Soil organic-matter, CEC, and moisture sensing with a portable NIR spectrophotometer[END_REF][START_REF] Udelhoven | Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: A feasibility study[END_REF]. In addition, most work on field VNIRS has been carried out on sample sets originating from small areas [START_REF] Gubler | Quantitative Estimations of Soil Properties by Visible and Near Infrared Spectroscopy -Applications for Laboratory and Field Measurements[END_REF].

The objective of the present study was to optimize spectrum acquisition for field VNIRS prediction of usual topsoil properties (including soil contents in calcium carbonate, organic matter, total nitrogen, available phosphorus, and exchangeable potassium) over a sample set representing the main areas of large-scale crop cultivation in France. Several scanning procedures were compared (on the soil surface; on raw or cut auger cores; on clods crumbled from cores, possibly mixed over several elementary sampling points, scanned after a delay, and dried; and finally on 2-mm sieved dried samples, which represent usual laboratory conditions); several usual preprocessing techniques were also tested (standard normal variate, multiplicative scatter correction, etc.).

Materials and methods

Study sites

Topsoil samples from 201 1-m² sites originating from six farmer's fields, each in a different French "département" (county), were studied:

-43 sites in a 8.3-ha field in Aube, center-east of France (48°23'28"N, 04°08'20"E); according to the World reference base (IUSS-ISRIC-FAO, 2006), the soil was a clayey Rendzic Leptosol (thus rich in calcium carbonate); -22 sites in a 4.8-ha field in Bas-Rhin, east of France (48°18'54"N, 07°37'22"E); the soil was a sandy loam Leptic Fluvisol; -33 sites in a 7.3-ha field in Essonne, north-east of France (48°18'47"N, 02°23'56"E); the soil was a silty Cambic Calcisol; -29 sites in a 7.0-ha field in Haute-Garonne, south of France (43°27'09"N, 01°38'31"E); the soil was a loamy Cambic Fluvisol, rich in calcium carbonate; -51 sites in a 14.1-ha field in the Landes, south-west of France (44°24'20"N, 00°34'13"O); the soil was a Podzol (thus sandy with topsoil rich in organic matter); -23 sites in a 5.7-ha field with in Loire-Atlantique, west of France (47°27'20"N, 00°58'00"O); the soil was a clayey sandy loam Gleyic Cambisol.

Sites were rather evenly distributed over the fields, and were sampled in winter (cold and rainy or even snowy season) between November 2011 and January 2012. Roughly, air temperature ranged from 0 to 15°C; soil water content was not determined.

Spectrum acquisition

Soil sample reflectance was measured from 350 to 2500 nm at 1 nm interval using a portable spectrophotometer ASD LabSpec 5000 (Analytical Spectral Devices, Boulder, CO, USA).

Each spectrum provided by the spectrometer actually resulted from the averaging of 32 coadded scans. In this device, light is delivered to the sample by a contact probe, which then collects the reflected signal and transmits it to the spectrometer. After every measurement the window of the contact probe was cleaned with a silk brush then lens paper and ethanol. A disc made of Spectralon (polytetrafluoroethylene) was used as white reference standard and its reflectance was measured every hour. At each 1-m² site, spectrum acquisition was carried out according to seven different procedures:

 SURFACE: one soil spectrum was acquired at each of the four corners of the site directly on the soil surface, which had been previously planned down using a knife, after possible residues had been removed; this rapid procedure characterized the very surface of the soil only, which might nevertheless be representative of underlying layers in tilled soils;  COREraw: using an auger, one 20-cm deep core was collected at each of the four corners of the site, and spectra were collected on the raw faces of the cores at depths of 2-3, 10-12 and 15-17 cm; this represented a total of 12 COREraw spectra per site;  COREcut: each core was cut so as to present a smooth surface on which spectra were acquired at depths of 2-3 cm, 10-12 and 15-17 cm, representing a total of 12 COREcut spectra per site; cutting aimed at avoiding possible noise due to core surface roughness;  CLODraw: clods resulting from the crumbling of one core were placed in a plastic tray and scanned in triplicate, representing a total of 12 CLODraw spectra per site;  CLODav: clods originating from the four cores of the site were grouped, mixed thoroughly, and four scans were carried out on this average sample;  CLODav+0.5: four scans of the average sample of the site were also carried out in the field after a half-day; before scan, samples had been kept in closed plastic bags within a bucket in the field (where they had presumably undergone limited change in moisture and temperature); the objective of this variant was to test whether spectrum acquisition could wait a few hours after sample collection (thus whether the spectrometer could be left, e.g., at the field edge, in order not to carry it in the field);  CLODav+1: four scans of the average sample of the site were also carried out in the field the next morning; before scan, samples had been kept in closed plastic bags within a car possibly in a garage overnight, then had been brought back in the field (they had presumably undergone limited change in moisture and temperature too); the objective of this variant was to test whether spectrum acquisition could wait until the next morning (thus whether the spectrometer could be left in a shed close to studied areas, for instance).

In situ measurements were also compared to laboratory measurements according to two procedures:

 CLODav_dry: the average sample of the site was air-dried at room temperature for two weeks then oven-dried at 40°C for one night, and then four scans were carried out;  CLODav_siev: the average sample of the site was gently crushed to pass a 2-mm sieve, then oven-dried at 40°C for one night, and spectra were acquired in duplicate.

Air temperature was 24°C in the laboratory. For each combination of site and procedure, all spectra were averaged for spectral analysis.

Spectrum analysis

Reflectance spectra were recorded as absorbance, which is the logarithm of the inverse of reflectance (log[1/R]). Data analysis was conducted using the WinISI III-V1.50e software (Foss NIRSystems/Tecator Infrasoft International, LLC, Silver Spring, MD, USA). The data sets were reduced by keeping the first of every eight consecutive spectral points [START_REF] Sørensen | Determination of clay and other soil properties by near infrared spectroscopy[END_REF][START_REF] Shenk | Population structuring of near infrared spectra and modified partial least square regression[END_REF], kept every fifth point). In addition, the 350-450 nm range was removed, due to noisy features, as well as transition domains between sensors (996-1004 and 1824-1834 nm).

Then preprocessing was used to eliminate or minimize additive and multiplicative effects caused for example by the heterogeneity of sample texture and physical properties, in order to improve the predictions of chemical properties [START_REF] Osborne | Practical NIR Spectroscopy with Applications in Food and Beverage Analysis[END_REF]. These effects include baseline shift, slope changes, variations in spectrum curvilinearity. Classic preprocessing techniques include calculating first and second derivatives, de-trending (D), standard normal variate transformation (SNV) and multiplicative scatter correction (MSC), which involves three options namely standard MSC, weighted MSC (WMSC), and inverse MSC (IMSC; [START_REF] Geladi | Linerization and scatter-correction for nearinfrared reflectance spectra of meat[END_REF]Barnes et al., 1989[START_REF] Dhanoa | The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra[END_REF][START_REF] Candolfi | The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra[END_REF]. In the present study, the preprocessing techniques used were None (no preprocessing), D, SNV, SNVD (i.e. both SNV and D), MSC, IMSC and WMSC, with different levels of derivatives, gap (interval used to calculate derivatives) and smoothing; this led to six combinations of these three factors: no derivatives with one-or five-point smoothing (denoted 0 0 1 and 0 0 5, respectively); first derivatives with five-, 15-or 20-point gap and five-point smoothing (1 5 5, 1 15 5 and 1 20 5), and second derivatives with five-point gap and smoothing (2 5 5). On the whole, 42 preprocessing methods were tested.

Modified partial least square (MPLS; [START_REF] Shenk | Population structuring of near infrared spectra and modified partial least square regression[END_REF] regression coupled with cross-validation was used to relate the spectra with the reference data. The MPLS regression combines principal component analysis and multiple linear regression in order to reduce a complex spectral matrix into a few orthogonal components, and has often been considered an appropriate statistical method for studying soil organic properties [START_REF] Fystro | The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis-NIR spectroscopy and comparative methods[END_REF][START_REF] Coûteaux | Near infrared reflectance spectroscopy for determination of organic matter fractions including microbial biomass in coniferous forest soils[END_REF][START_REF] Brunet | Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity[END_REF]. Cross-validation is a technique which allows testing if a statistical model could be generalized to an independent data set. In the present case, this technique consisted of partitioning the dataset into four subsets, performing the calibration on three subsets and validating it on the fourth, the operation being repeated three times by rotating the subsets used for validation and calibration. The results were then averaged to produce a single estimation. The parameters used to assess and compare the performances of the prediction models were the coefficient of determination (R 2 ) and the slope of the linear regression between measured and predicted values, the standard error of cross-validation (SECV), the ratio of the standard deviation (SD) to SECV (denoted RPD) and the bias, which is the mean residue. The number of MPLS components above which SECV did not decrease meaningfully anymore was retained for calibration [START_REF] Bjørsvik | Data analysis: calibration of NIR instruments by PLS regression[END_REF]. Poorly calibrated samples (i.e. with residual > 2.5 SECV) were considered outliers. However, to allow valuable comparison between scanning procedures and between preprocessing methods, the number of calibration outliers removed was maintained fixed for a given soil property. For calcium carbonate and exchangeable potassium, respectively, the number of calibration outliers was three and seven in general, and all were removed. For the other properties studied, the number of calibration outliers was much higher, up to 20 sometimes; the number of calibration outliers removed was limited to 5% of the sample population, which represented ten samples for total nitrogen, organic matter and available phosphorus.

External validation was not carried out because the objective of the study was not to build robust calibrations but to identify the most appropriate scanning procedure. In addition, the six studied fields represented different mineralogical and textural contexts, thus using one as external validation set was not relevant. Once the scanning procedure identified, the calibration set will be completed by other samples from new sites, and testing robustness will be more appropriate then.

Chemical analysis

Determination of soil contents in calcium carbonate, organic carbon, total nitrogen, available phosphorus and exchangeable potassium were carried out on aliquots of COREav samples dried at 38°C then gently crushed and sieved at 2 mm. Calcium carbonate was measured by a volumetric method with a Bernard calcimeter according to the French procedure NF ISO 10693, which involves the addition of hydrochloric acid and the measurement of the volume of carbon dioxide released (AFNOR, 1995a). Total nitrogen was determined on finely ground samples by dry combustion using an elemental analyzer, according to the French procedure NF ISO 13878; all forms of nitrogen are volatilized by combustion at 900°C to form dinitrogen (N 2 ), which is then measured by gas chromatography using a thermal conductivity detector (AFNOR, 1998a). Organic carbon content was determined by sulfochromic oxidation (oxidation with potassium dichromate and sulfuric acid) then spectrocolorimetry, according to the French procedure NF ISO 14235; it was then converted into soil organic matter content after multiplication by 1.72 (AFNOR, 1998b). Available phosphorous was determined by the Olsen method, which involves extraction using a solution of sodium bicarbonate then measurement by spectrocolorimetry, according to the French procedure NF ISO 11263 (AFNOR, 1995b). Exchangeable potassium was determined after extraction in a solution of ammonium acetate and measurement by inductively coupled plasma (ICP) analysis, according to the French procedure NF X31-108 (AFNOR, 2002).

Results and discussion

Conventional data

The studied topsoil population displayed wide ranges of soil contents in calcium carbonate (from 0 to 85 g 100 g -1 dry soil), organic matter (1 to 9 g 100 g -1 ), total nitrogen (0.04 to 0.46 g kg -1 ), available phosphorus (18 to 178 mg P 2 O 5 kg -1 ) and exchangeable potassium (31 to 684 mg K 2 O kg -1 ; Table 1). This was consistent with the diversity of the soils sampled, which originated from six very different regions of France.

Overall prediction accuracy

For each soil property and each sampling procedure, the 42 above-mentioned preprocessing methods were compared. For a given soil property, a similar number of outliers was set for all sampling and preprocessing procedures, and never exceeded 5% of the total number of samples. Considering the preprocessing method that yielded the best predictions for each scanning procedure, the range of RPD values was 6.9-9.1 for calcium carbonate, 2.5-3.0 for total nitrogen, 2.1-2.8 for organic matter, 1.6-1.8 for available phosphorous, and 2.9-3.2 for exchangeable potassium (Table 2). According to [START_REF] Chang | Near-infrared reflectance spectroscopy -Principal components regression analyses of soil properties[END_REF] and [START_REF] Dunn | The potential of near infrared reflectance spectroscopy for soil analysisa case study from the Riverine Plain of southeastern Australia[END_REF], RPD = 2 could be considered a threshold for accurate prediction of soil properties. Whatever the scanning procedure, accurate predictions could thus be achieved for all studied properties except for available phosphorus (Figure 1). The prediction of soil content in calcium carbonate was excellent whatever the scanning and preprocessing procedures, suggesting that conventional laboratory determination of this property is not useful anymore, except for maintaining calibration databases.

Prediction accuracy for calcium carbonate was much higher than that reported in field conditions by [START_REF] Lagacherie | Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements[END_REF] for a set of vineyard topsoil samples in southern France (RPD estimation between1.3 to 2.0 depending on scanning procedure; cross-validation); their less accurate results might be attributed to statistical procedures (using a narrow range of wavelengths only), to variable soil stoniness, and to possibly variable field illumination conditions (the study was carried out with a spectroradiometer). Comparable accuracy was achieved for inorganic carbon by [START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF]; RPD = 2.0; external validation) over a sample set collected at different depth layers in six fields from two counties in Central Texas. This result was obtained for external validation, which is more demanding in general than cross-validation; but it is likely that variable sample depth increased set heterogeneity, which has been shown to affect prediction accuracy negatively [START_REF] Brunet | Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity[END_REF]. The same reasons could explain why [START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF] achieved poor predictions of soil organic matter content (RPD = 1.5). Studying Israeli soils, [START_REF] Ben-Dor | A novel method of classifying soil profiles in the field using optical means[END_REF] obtained accurate external validation of organic matter content (RPD = 4.1) from field VNIR spectra using calibration built with a mix of spectra acquired in field conditions and on sieved dried samples; but it seems that many outliers were removed, which could have contributed to this good result. [START_REF] Stevens | Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils[END_REF] also reported good field VNIRS predictions of soil organic matter content for a regional sample set (50 km²) in southern Belgium (cross-validation; RPD = 2.8). In the Manawatu region of New Zealand, [START_REF] Kusumo | Predicting soil carbon and nitrogen concentrations and pasture root densities from proximally sensed soil spectral reflectance[END_REF] found much more accurate field VNIRS predictions of soil organic matter and nitrogen (cross-validation; RPD = 7.3 and 6.3, respectively). Two hypotheses might explain these excellent results: all studied samples originated from recent fluvial deposits under pastures, which probably resulted in set homogeneity thus improved VNIRS predictions (cf. [START_REF] Brunet | Determination of carbon and nitrogen contents in Alfisols, Oxisols and Ultisols from Africa and Brazil using NIRS analysis: Effects of sample grinding and set heterogeneity[END_REF]; in addition, [START_REF] Kusumo | Predicting soil carbon and nitrogen concentrations and pasture root densities from proximally sensed soil spectral reflectance[END_REF] studied much smaller cores than in the present study (15 mm high × 46 mm wide vs. 200 mm high × 80 mm wide, respectively), and even though they possibly made fewer spectrum replicates (10 vs. 4 to 12), their scans were probably more representative of samples analyzed conventionally (reference chemical analyses). Over a topsoil sample set collected along a 130-km transect in South Africa, [START_REF] Nocita | Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa[END_REF] achieved field VNIRS prediction of organic matter with similar accuracy than in the present study (RPD = 2.4, with scan from the soil surface apparently; cross-validation). Over a fresh topsoil set from northern Belgium, [START_REF] Mouazen | Effect of wavelength range on the measurement accuracy of some selected soil constituents using visual-near infrared spectroscopy[END_REF] obtained less accurate VNIRS predictions of total nitrogen, available phosphorus and exchangeable potassium than in the present study, though set homogeneity was higher (RPD = 1.8, 1.4, and 1.2, respectively; cross-validation); scanning procedure was not specified. Few works have addressed field VNIRS predictions of available phosphorus or exchangeable potassium. However, as regarded laboratory VNIRS predictions over heterogeneous soil sample sets, several reviews reported good results for exchangeable potassium but poor ones for available phosphorus [START_REF] Malley | Application in analysis of soils[END_REF][START_REF] Cécillon | Assessment and monitoring of soil quality using near infrared reflectance spectroscopy (NIRS)[END_REF][START_REF] Stenberg | Visible and near infrared spectroscopy in soil science[END_REF]. Regarding organic matter content, the under-prediction that was noticed in the present study at high values (> 5 g 100 g -1 ) was also observed by [START_REF] Ben-Dor | Near-infrared analysis as a rapide method to simultaneaously evaluate several soil properties[END_REF] over an air-dried and 2-mm sieved sample set from Israel representing a wide range of organic matter content. The authors considered that the sample set could be divided into two subsets, one with samples poor in organic matter (0-4 g 100g -1 ), including highly decomposed organic matter, and the other with samples richer in organic matter (4-14 g 100g -1 ), including less decomposed organic matter.

Effect of the scanning procedure

Spectrum acquisition procedure had an effect on prediction accuracy. Indeed, best RPD achieved using a given scanning procedure could increase noticeably when using another scanning procedure: by 2.2 for calcium carbonate, 0.5 for total nitrogen, 0.7 for organic matter, 0.3 for available phosphorus, and 0.4 for exchangeable potassium (Table 2). This possible increase was crucial for organic matter, as best SECV ranged from 0.42 to 0.55 g 100 g -1 depending on the way spectrum were acquired. This possible increase was useful but less critical for calcium carbonate, total nitrogen and exchangeable potassium, which could be accurately predicted whatever the scanning procedure (RPD ≥ 2.5); this was also less critical for available phosphorus, which prediction was always poor.

CLODav_siev yielded best predictions for three out of the five soil properties considered, which is consistent with the facts that this procedure reduced noise (through drying) and homogenized the samples (through crushing and 2-mm sieving). COREraw yielded best predictions for the two other properties, and was in the top four scanning procedures for all properties (Table 2). When removing laboratory procedures (CLODav-dry and CLODavsiev), COREraw was even in the top two scanning procedures for all properties. At least for the spectrometer, the sample population and the soil properties considered, COREraw could thus be seen as the most appropriate procedure for acquiring soil spectra in the field, though COREcut and CLODraw yielded slightly better predictions for total nitrogen and exchangeable potassium, respectively.

A possible reason for poorer performance of SURFACE, CLODav, CLODav+0.5 and CLODav+1 could be the smaller number of replicates that were carried out for these procedures (four) than for other field procedures (12). Samples scanned for CLODav, CLODav+0.5 and CLODav+1 were averaged over a site and thus more homogeneous a priori, but apparently this did not offset the smaller number of replicates. In addition, rather poor predictions for SURFACE resulted likely from the fact that the scan addressed only the very surface of the sample that was analyzed conventionally (0-20 cm); thus scanned and analyzed samples were not similar. Smaller number of replicates was also the probable reason why laboratory conditions, though they involved drying (CLODav-dry) and possible 2-mm sieving (CLODav-siev), did not always yield the best predictions. COREraw generally outperformed CLODraw, possibly due to its denser thus more homogeneous structure.

The results of the present study confirmed that the performance of field and laboratory VNIRS may be similar, as reported for organic carbon by [START_REF] Stevens | Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils[END_REF]RPD = 2.1 vs. 2.0, respectively). However, these authors used a spectroradiometer and scanned a spot size of 0.45 m in the field (measurement height of 1 m), but a contact probe and a small soil sample in the laboratory (with artificial illumination). Similar numbers of replicates were made for both field and laboratory measurements, but the former incorporated much more soil variability than the latter. Though not underlined by [START_REF] Stevens | Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils[END_REF], this represented an advantage that might be compared in the present study to the larger number of replicates made in the field than in the laboratory. Thus the hypothesis that similar prediction accuracy for field and laboratory VNIRS resulted from larger number of replicates in the field than in the laboratory could not be dismissed. However, as field work allows replication more easily than lab work, this point should be considered a difference between approaches rather than an artifact. [START_REF] Stevens | Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils[END_REF] considered that achieving similar VNIRS performances in the field than in the laboratory was counterintuitive. Indeed, spectrum acquisition in the field generally involves variable soil moisture and temperature, the presence of coarse mineral and organic particles (which are scanned in the field but removed by sieving, thus not taken into account by chemical analyses and laboratory VNIRS), and possibly vegetation cover [START_REF] Stenberg | Visible and near infrared spectroscopy in soil science[END_REF]. It might however be assumed that in some respects field conditions are beneficial, due to higher sample density and homogeneity, especially when cores are scanned, and/or due to higher cohesion in moist conditions, which both could improve the relationship between sample composition and absorbance. In total, these positive and negative factors could offset each other, and would result in similar prediction accuracy in field and laboratory conditions. [START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF], who obtained more accurate VNIRS predictions of organic carbon content on six fields from two Texas counties using dried cores than 2-mm sieved air-dried samples (RPD = 1.9 vs. 1.7, respectively ; external validation), indeed argued that higher bulk density in the former might result in stronger reflectance signal than in the latter. Viscarra Rossel et al. ( 2009) also invoked higher density, along with smoother surface, which would produce higher reflectance, to explain better VNIRS prediction of clay content using spectra collected on pit walls than using 2-mm sieved dried samples. However, [START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF] reported less accurate VNIRS predictions of inorganic carbon on dried cores than on 2-mm sieved air-dried samples (RPD = 2.2 vs. 2.4), possibly due to secondary carbonate accumulation in some samples. Moreover, these authors obtained less accurate predictions using field-moist cores than 2-mm sieved air-dried samples for organic carbon (RPD = 1.5 vs. 1.7) and inorganic carbon (RPD = 2.0 vs. 2.4). [START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF] concluded that soil heterogeneity, at the core scale, did not reduce VNIRS effectiveness for predicting organic carbon; nevertheless it reduced effectiveness for predicting inorganic carbon, which was more correctly addressed on homogenized 2-mm sieved samples. They also underlined that airdrying improved prediction accuracy for organic and inorganic carbon. Over a topsoil sample set from northern Belgium, [START_REF] Mouazen | Effect of wavelength range on the measurement accuracy of some selected soil constituents using visual-near infrared spectroscopy[END_REF] also achieved less accurate VNIRS predictions of total nitrogen, available phosphorus and exchangeable potassium using spectra from fresh and wet samples than from dried and sieved samples RPD = 1.8 vs. 2.4,1.4 vs. 1.7,and 1.2 vs. 1.3, respectively; scanning procedure was not specified).

Similarly, [START_REF] Nocita | Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa[END_REF] reported less accurate VNIRS predictions of organic carbon using spectra acquired on the topsoil surface (apparently, because the scanning procedure was not specified) than on 2-mm sieved air-dried samples (cross-validation; RPD = 2.4 vs. 2.8).

As regarded moisture conditions specifically, on a loamy sample set originating from different depth layers in one field, [START_REF] Chang | Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties[END_REF] found less accurate NIRS predictions of inorganic carbon, total nitrogen, and organic carbon using spectra from field-moist samples than from air-dried, both 2-mm sieved (cross-validation; RPD = 5.6 vs. 6.1, 3.6 vs. 4.0, 3.2 vs.

3.3, respectively); they did not propose possible explanations. By contrast, studying a wide range of Swedish soils, [START_REF] Stenberg | Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon[END_REF] reported that rewetting of 2-mm sieved dry samples (20% or 30% v/v) improved VNIRS prediction of soil organic matter by 20-30%, and that improvement was mainly for the more sandy samples. Though he recognized that mechanisms behind this improvement remained largely unexplained, he suggested that rewetting could reduce the reflectance contrast between dark organic matter and highly reflecting sand particles (mainly featureless quartz and feldspar). These results involved standardized soil rewetting, which of course was not achieved in the present study; nevertheless spectrum acquisition in the field occurred in winter, in moist but rarely saturated conditions (which render field work very tedious), therefore soil moisture was not very variable. Thus negative effects of rather variable moisture conditions could possibly be offset by positive effects of higher moisture content in the field than in the laboratory. These considerations, and the paucity of explanations proposed in the cited papers, underline the need for fundamental works on interactions between light and soil, and possibly water, in order to help understand the effects of soil sample density and moisture on NIRS prediction accuracy.

Briefly, the present study and literature results suggested that VNIRS predictions of soil properties might have comparable accuracy using spectra acquired in the field or on 2-mm sieved air-dried samples. For total nitrogen, available phosphorus and exchangeable potassium, all studies achieved more (sometimes slightly) accurate predictions on sieved dried samples [START_REF] Chang | Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties[END_REF][START_REF] Mouazen | Effect of wavelength range on the measurement accuracy of some selected soil constituents using visual-near infrared spectroscopy[END_REF]this work); for calcium carbonate, most studies obtained better results on sieved dried samples [START_REF] Chang | Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties[END_REF][START_REF] Lagacherie | Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements[END_REF][START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF] but not the present study); for organic matter, half of the studies had better predictions in the field or on wet samples [START_REF] Chang | Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties[END_REF][START_REF] Stevens | Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils[END_REF][START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF][START_REF] Stenberg | Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbon[END_REF][START_REF] Nocita | Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa[END_REF]this work); for clay (which was not studied here), most studies obtained better results in the field (Waiser et al., 2007;[START_REF] Lagacherie | Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements[END_REF][START_REF] Viscarra Rossel | In situ measurements of soil colour, mineral composition and clay content by vis-NIR spectroscopy[END_REF]. Differences between studies could be attributed to variable field scanning procedures, to field conditions (very variable, always dry, always wet), to the sample set (homogeneous or heterogeneous as regarded e.g. mineralogy, texture, depth), chemometrics (type of regression, possible outlier removing, mathematical preprocessing, cross or independent validation, etc.), to procedures carried out in the lab, to possible replications, and probably to other sources.

Effect of the preprocessing method

Spectrum preprocessing influenced prediction accuracy noticeably in general, but to an extent that depended on the variable considered and on spectrum acquisition procedure (Figure 2).

Second derivatives yielded poor predictions and are not considered here. The effect of preprocessing on prediction accuracy was particularly important with CLODav_siev, and to a lesser extent, with CLODav_dry (i.e. on dry samples), and in some other cases (e.g. for organic matter with CLODraw). Thus choosing the preprocessing procedure mattered, especially in laboratory conditions. Considering COREraw and COREcut, which were in general the most appropriate scanning procedures in the field, best predictions were achieved (data not shown):

-for calcium carbonate, with SNVD 0 and 1 (i.e. SNVD with no or first derivatives), and SNV 1, MSC 1, WMSC 1 and IMSC 1 (first derivatives); SNVD 1 provided especially good results; -for total nitrogen, with SNV 0 and 1, MSC 0 and 1 (no or first derivatives), and D 0 (no derivatives); -for organic matter, with MSC 1 (first derivatives) and SNV 0 (no derivatives); -for available phosphorus, with None 0 and 1 (no or first derivatives), D 0 (no derivatives), SNV 0 and 1, IMSC 0 and 1, WMSC 0 and 1, SNVD 0 and 1, and MSC 0 and 1 (no or first derivatives); -for exchangeable potassium, with None 0 and D 0 (no derivatives); None 0 provided especially good results. This was estimated according to the absence of significant difference with the lowest SECV achieved for the scanning procedure and soil property considered (Fisher test at p < 0.05).

In addition, when using first derivatives, large gaps (15 or 20) often yielded better results than a small one (5).

In short, MSC 1, and to a lesser extent SNV 1, were useful for predicting calcium carbonate, total nitrogen and organic matter (but the former was well predicted whatever the pretreatment); and None 0, and to a lesser extent D 0, for predicting exchangeable potassium (and available phosphorus, which was nevertheless poorly predicted). Comparing None and MSC with first or second derivatives, [START_REF] Ben-Dor | A novel method of classifying soil profiles in the field using optical means[END_REF] also selected MSC with first derivative to optimize field VNIRS prediction of organic matter. [START_REF] Morgan | Simulated in situ characterization of soil organic and inorganic carbon with visible near-infrared diffuse reflectance spectroscopy[END_REF] also obtained better field VNIRS prediction of organic carbon with first derivatives than with second or without derivatives; however, they obtained better predictions of inorganic carbon without derivatives.

Conclusion

Good VNIRS cross-validation could be achieved in the field for calcium carbonate, total nitrogen, organic matter and exchangeable potassium, over a topsoil sample set representing the main areas of large-scale crop cultivation in France: depending on scanning procedure and mathematical pretreatment, RPD reached up to 9.1, 2.9, 2.8 and 3.0, respectively. Very good results for calcium carbonate suggested that conventional laboratory determination is not useful anymore for this property, except for maintaining calibration databases. By contrast, available phosphorous was poorly predicted (RPD ≤ 1.7), but this soil property is hardly predictable using laboratory VNIRS too, except over very homogeneous sample sets (e.g. samples originating from one field).

The main objective of the work was to identify the most appropriate scanning procedure for field VNIRS prediction of soil properties. Indeed, prediction accuracy depended on the spectrum acquisition method, but to an extent that varied with the property: choosing the appropriate procedure was crucial for organic matter (the right procedure could increase RPD by 0.7); it was useful but less crucial for calcium carbonate (RPD could increase by 2.2; but all scanning methods yielded excellent predictions), total nitrogen (by 0.5), available phosphorus (by 0.3), and exchangeable potassium (by 0.4). Very generally, best predictions were achieved when spectra were acquired on cores, either raw or cut, while predictions from spectra acquired on the soil surface or on clods (resulting from core crumbling) were less accurate.

Spectra acquired in the field are affected by variations in moisture, temperature, stoniness, etc., which render calibration more difficult a priori. Counterintuitively, comparable prediction accuracies were achieved for laboratory (i.e. on 2-mm sieved air-dried samples) and field VNIRS (on cores). This could be due, at last partly, to the larger number of replicates carried out in the field than in the lab (4 to 12 vs. 2 to 4). As replication is easier in the field than in the lab, this discrepancy represented a difference between field and lab approaches rather than an artifact. Another possible reason for similar prediction accuracies for field and lab VNIRS could be the higher cohesion of samples scanned in the field (in relation with higher density and moisture), which would be beneficial for calibrations and would thus offset the detrimental effects of variable field conditions. Fundamental works on light-soil interactions are needed to clarify the mechanisms involved.

In addition, mathematic preprocessing of spectra affected calibrations, but to a lesser extent using field-than laboratory-acquired spectra. For spectra acquired on soil cores (in the field), best calibrations of calcium carbonate, total nitrogen and organic matter were achieved with MSC, and to a lesser extent SNV, both with first derivatives; best calibrations of exchangeable potassium and available phosphorus were achieved without pretreatment, and with detrend without derivatives to a lesser extent. Using second derivatives always yielded the poorest calibrations.

Acknowledgements

The authors thank two anonymous reviewers for their helpfull comments. Barthès, B.G., Brunet, D., Ferrer, H., Chotte, J.L., Feller, C., 2006 Minimum, maximum and mean cross-validation RPD for each soil property studied and every scanning procedure, depending on preprocessing procedures (second derivatives being excluded due to very poor predictions). The scanning procedures were (cf. section 2.2): on the soil surface (SURFACE); on raw/cut auger cores (COREraw/cut); on clods crumbled from cores separately (CLODraw) or together (CLODav), after a half-day (CLODav+0.5; field), the next morning (CLODav+1; field), after drying (CLODav_dry; lab), and after drying and sieving (CLODav_siev; lab).

  Figure2. Minimum, maximum and mean cross-validation RPD for each soil property studied and every scanning procedure, depending on preprocessing procedures (second derivatives being excluded due to very poor predictions). The scanning procedures were (cf. section 2.2): on the soil surface (SURFACE); on raw/cut auger cores (COREraw/cut); on clods crumbled from cores separately (CLODraw) or together (CLODav), after a half-day (CLODav+0.5; field), the next morning (CLODav+1; field), after drying (CLODav_dry; lab), and after drying and sieving (CLODav_siev; lab).

  AFNOR (Association Française de Normalisation), 1995a. Qualité du Sol -Détermination de la Teneur en Carbonate -Méthode Volumétrique. NF ISO 10693. AFNOR, Paris. AFNOR (Association Française de Normalisation), 1995b. Qualité du sol -Dosage du Phosphore -Dosage Spectrométrique du Phosphore Soluble dans une Solution d'Hydrogénocarbonate de Sodium. NF ISO 11263. AFNOR, Paris. AFNOR (Association Française de Normalisation), 1998a. Qualité du Sol -Détermination de la Teneur Totale en Azote par Combustion Sèche ("Analyse Elémentaire"). NF ISO 13878. AFNOR, Paris. AFNOR (Association Française de Normalisation), 1998b. Qualité du Sol -Dosage du Carbone Organique par Oxydation Sulfochromique. NF ISO 14235. AFNOR, Paris. AFNOR (Association Française de Normalisation), 2002. Qualité des Sols -Détermination des Cations Ca ++ , Mg ++ , K + , Na + Extractibles par l'Acétate d'Ammonium -Méthode par Agitation. NF X31-108. AFNOR, Paris. Barnes, R. J., Dhanoa, M.S., Lister, S.J., 1989: Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy 43, 772-777.

Table 1 .

 1 . Determination of total carbon and nitrogen content in a range of tropical soils using near infrared spectroscopy: influence of replication and sample grinding and drying. Journal of Near Infrared Spectroscopy 14, 341-348. Waiser, T.H.,Morgan, C.L.S., Brown, D.J., Hallmark, C.T., 2007. In situ characterization of soil clay content with visible near-infrared diffuse reflectance spectroscopy. Soil Science Society of America Journal 71, 389-396. Distribution of measured properties over the studied topsoil (0-20 cm) sample population.Figure1.Comparison between measured and cross-validation predicted values of soil contents in calcium carbonate, total nitrogen, organic matter, available phosphorous, and exchangeable potassium at 0-20 cm depth (mentioning the most appropriate scanning procedure in the field, either COREraw on raw cores, COREcut on cut cores, or CLODraw on clods crumbled from cores separately, cf. section 2.2).

	Soil property	Min	Max	Mean	SD a
	Calcium carbonate (g 100 g -1 )	0.0	84.9	16.1	27.3
	Total nitrogen (g kg -1 )	0.04	0.46	0.16	0.07
	Organic matter (g 100 g -1 )	1.07	9.23	3.22	1.42
	Available phosphorous (mg P 2 O 5 kg -1 )	17.5	177.5	65.5	31.0
	Exchangeable potassium (mg K 2 O kg -1 )	31.0	684.0	236.5	156.0

a SD is the standard deviation.

Table 2. Cross-validation statistics using preprocessing methods that yielded best predictions of the soils properties considered at 0-20 cm depth (SECV and bias are in the unit of the soil property; RPD, slope and R² are unitless; for preprocessing,see section 2.3.). The scanning procedures were (cf. section 2.2): on the soil surface (SURFACE); on raw/cut auger cores (COREraw/cut); on clods crumbled from cores separately (CLODraw) or together (CLODav), after a half-day (CLODav+0.5; field), the next morning (CLODav+1; field), after drying (CLODav_dry; lab), and after drying and sieving (CLODav_siev; lab).


MD Emma Rubenstein

MD Romaric Larcher

MD Romain Altwegg

MD Yves-Marie Pers

Exceptional Paraspinal Localization of Myositis in a Patient With Crohn Disease Under Tumor Necrosis Factor α Inhibitor Treatment: A Case Report

-CLINICAL IMAGE

We report the case of a 22-year-old female patient who was hospitalized for intense inflammatory rachialgia evolving for 5 months and resistant to nonsteroidal anti-inflammatory drugs. There was no fever and no weight loss. The medical history consisted of Crohn disease (CD) diagnosed 5 years earlier, treated with budesonide and golimumab for the last 8 months, after the occurrence of immunoallergic adverse events with other tumor necrosis factor α (TNF-α) inhibitors and azathioprine. There were no peripheralmusculoskeletal symptoms, no neurological or cutaneous signs, and no signs of activity of CD. Biological tests showed a Creactive protein at 77 mg/L, normal CK, and aldolase counts. Anti-SRP (anti-signalrecognition particle) autoantibodies were positive.

The magnetic resonance imaging showed T2 short tau inversion recovery hyperintensity involving the thoracic and lumbar paraspinal muscles, but no abnormal signal in the thighs. There was no sign of spondylitis. The paraspinal muscle biopsy revealed an inflammatory infiltrate with T lymphocytes and macrophages, without any necrosis. Immunohistochemistry showed a diffuse positivity of class I HLA and a focal presence of C5b-9. The treatment of this polymyositis consisted of 3 pulses of methylprednisolone followed by oral prednisolone 1 mg/kg per day. It rapidly permitted a reduction of pain and biologic inflammation. Golimumab treatment was continued for CD. A steroid-sparing treatment consisting of intravenous polyvalent immunoglobulins monthly was implemented, allowing a sustained remission. No clinical or biological recurrence occurred al most 3 years later after the initial flare (Fig. ).

The diagnosis and classification of myositis can be difficult, as there are overlapping forms and associations with other systemic diseases.1 Here, the clinicobiological presentation was uncommon, with myalgia as a main complaint rather than muscle weakness, and with the absence of CK elevation. In contrast, anti-SRP antibodies are usually associated with severe and rapidly progressive muscle weakness with very high CK levels and a necrotizing form of myositis.1 In this case, the biopsy rather showed suggestive signs of polymyositis, such as a ubiquitous expression of class I HLA, and mononuclear inflammatory cell infiltrates in the endomysium.1 The peculiarity of this case also relied on the rare paraspinal localization of myositis, as it usually rather involves the proximal limbmuscles.1 There are few reports in the literature of paraspinal inflammatory myopathy.2-4 In contrast with our case, all of them were revealed with camptocormia and dropped-head syndrome due to muscle weakness, rather than pain, and all of them had elevated CK. Some antinuclear antibodies were reported but no anti-SRP antibodies.

In the context of CD with a TNF-α antagonist treatment, the occurrence of myositis raised the question of an association with either the disease or the treatment. The association of myositis with other autoimmune diseases (lupus, rheumatoid arthritis, Sjögren syndrome) is widely described, and it can also occur in association with CD.2 By contrast, myositis was treated as an independent entity, permitting a sustained remission. There are several reported cases of new-onset dermatomyositis or polymyositis during anti-TNF-α treatment.5 In our case, golimumab treatment could not be interrupted, but it did not impede the remission obtained with immunoglobulins.