Didier Brunet

Bernard G Barthès

Jean-Luc Chotte

Christian Feller

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

Keywords: Near infrared reflectance spectroscopy (NIRS), Soil, Carbon, Nitrogen, Texture

Near infrared reflectance spectroscopy (NIRS) is a rapid and low-cost method that has been reported to provide accurate prediction of soil total carbon (Ct) and nitrogen (Nt) contents.

samples, with 6-80% clay). The total set could be divided into a coarse-textured set, including samples from Burkina Faso and Benin mainly, and a clayey set, including samples from Brazil mainly. Calibration and validation procedures were carried out within the total set and within each of the two texture sets and the three localised sets (Burkina Faso, Benin, and Brazil).

Prediction of Ct using first derivatives with SNV (standard normal variate) scatter correction, which was the most appropriate spectrum preprocessing method, was in general more accurate for ground than for sieved samples. The difference was decisive when the determination was carried out on the localised clayey set, moderate on total and texture sets, and unclear on localised coarse-textured sets. Prediction of Nt was less clearly affected by sample preparation, though grinding was useful for the localised clayey set and harmful for localised coarse-textured ones. Thus the effect of sample preparation on prediction accuracy depended on soil texture, which may explain the contradictory results reported in the literature, often referring to narrow soil ranges. Using the same preprocessing method, the accuracy of Ct prediction generally increased with set homogeneity (i.e. from total to texture sets and from texture to localised sets), providing that clayey sets were ground. This agreed with deductions drawn from comparison between earlier studies carried out with either homogeneous or heterogeneous sets. The effect of set homogeneity on Nt prediction could not, however, be clearly demonstrated by this study. In conclusion, the study confirmed the interest of NIRS for the rapid and accurate determination of Ct and Nt, especially when homogeneous sets including ground samples were used.

Introduction

Concerns about global climate change have renewed interest in carbon cycle studies, which often require numerous determinations of soil carbon content. Standard procedures for measuring soil carbon being time-consuming and expensive, much attention is given to possible alternatives such as near infrared reflectance spectroscopy (NIRS). NIRS is a physical non-destructive, rapid, reproducible and low-cost method that characterises materials according to their reflectance in the wavelength range between 800 and 2500 nm. Reflectance signals are produced by flexural and longitudinal vibrations in bonds between C, N, H, O, P, and S atoms, and provide information about the proportion of each element in the materials analysed. As organic materials are complex mixtures, their spectra result from the addition of elementary absorptions corresponding to each bond and are not directly informative.

Calibration is a statistical process that relates spectral information to sample properties measured by conventional methods. Calibration equations are then used to predict these properties for other samples.

NIRS was first used for the assessment of moisture content of seeds and other plant products [START_REF] Hart | Determination of the moisture content of seeds by nearinfrared spectrophotometry of their methanol extracts[END_REF], then for the evaluation of seed and forage quality [START_REF] Williams | Applications of near-infrared reflectance spectroscopy to analysis of cereal grains and oilseeds[END_REF]. Its application to soils has been mentioned from the 1960s [START_REF] Bowers | Reflection of radiant energy from soils[END_REF], and studies reporting accurate prediction of soil carbon and nitrogen contents using NIRS were published in the 1970s [START_REF] Al-Abbas | Relating organic matter and clay content to the multispectral radiance of soils[END_REF] and have been on the increase since 1990 [START_REF] Morra | Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy[END_REF][START_REF] Henderson | High dimensional reflectance analysis of soil organic matter[END_REF][START_REF] Sudduth | Soil organic matter, CEC, and moisture sensing with a portable NIR spectrophotometer[END_REF]. However, some methodological aspects have not been addressed extensively. There are conflicting reports in the literature concerning the need for grinding the samples. Several papers have underlined the usefulness of grinding samples for NIRS determination of soil carbon content [START_REF] Dalal | Simultaneous determination of moisture, organic carbon and total nitrogen by near infrared reflectance spectrophotometry[END_REF][START_REF] Reeves | The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils[END_REF][START_REF] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF]. These confirmed studies carried out on vegetative matter [START_REF] Blosser | Factors affecting analysis of the chemical composition of tall fescue with near infrared reflectance spectroscopy[END_REF]. There are also reports in the literature of accurate NIRS prediction of soil carbon content using unground samples [START_REF] Chang | Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[END_REF][START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[END_REF]Morón and Cozzolino, 2004) and some have even suggested grinding the samples did not improve the prediction accuracy [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]. As soil grinding is often tedious and time-consuming, it is clear that the usefulness of this process should be evaluated. Moreover, though several studies have been carried out on heterogeneous sets including wide ranges of soil types [START_REF] Ben-Dor | Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[END_REF][START_REF] Chang | Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[END_REF][START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[END_REF], several authors consider the reliability of NIRS prediction questionable when studying heterogeneous sample sets [START_REF] Sudduth | Geographic operating range evaluation of a NIR soil sensor[END_REF][START_REF] Brown | Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana[END_REF]. More information concerning the effect of set heterogeneity on the accuracy of NIRS prediction is needed in order to apply NIRS more generally to soil characterisation. There also seems to be some confusion about the data preprocessing, such as derivation and scatter correction in order to optimise spectral features. For example, for NIRS prediction of soil carbon content, many authors used the first derivatives of the spectra [START_REF] Chang | Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[END_REF][START_REF] Reeves | The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils[END_REF][START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[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] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF]Morón and Cozzolino, 2004) while others preferred the second derivatives [START_REF] Salgó | Characterisation of soils by the near infrared technique[END_REF][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].

This paper assesses the effect of sample preparation (0.2 mm grinding vs. 2 mm sieving) and set heterogeneity on the accuracy of NIRS determination of soil total carbon (Ct) and soil total nitrogen (Nt) contents for a range of Alfisols, Oxisols and Ultisols from Africa and Brazil. The effects of data preprocessing were also investigated (first vs. second derivatives of the spectra, and standard normal variate with or without detrending).

Materials and methods

Soil samples

The total set studied included 297 soil samples originating from Benin, Burkina Faso, Congo, Senegal, and Brazil, collected at depths ranging from 0-5 to 55-60 or 50-70 cm (Table 1).

Samples were generally collected from pits, using cylinders, and were sometimes mixed to form composite samples (e.g. Congo and Brazil-SP). In nearby preparative laboratories, samples were spread out in trays and air dried at room temperature, before being stored in plastic bags or cardboard boxes, then transported to Montpellier, France. Then samples were gently crushed using a mortar and pestle and sieved through a 2 mm mesh. Aliquots were finely ground to pass a 0.2 mm mesh, also using a mortar and pestle.

The soils were all "low activity clay" soils, with clay minerals dominated by 1:1 phyllites, such as kaolinite, associated with more or less crystallised Fe, Al, Mn oxides and/or hydroxides. They belonged to three orders: Oxisols, Ultisols and Alfisols (Soil Survey Staff, 1999). In order to study the effect of set heterogeneity on NIRS prediction, the total set of samples was divided into two subsets according to soil texture: the first subset included 199 coarse-textured samples from Benin, Burkina Faso and Senegal (6% to 35% clay) and the second subset, 98 clayey samples from Brazil and Congo (43% to 80% clay). The coarsetextured subset was denoted SA (for sandy), the clayey one CL, and the total set CLSA. Each subset was further divided according to the source country: Burkina Faso (BF, 79 samples), Benin (BE, 116 samples), and Brazil (BR, 87 samples); countries including a small number of samples (Congo, Senegal) were not considered individually. The sets including 2 mm sieved samples were denoted CLSA 2 , SA 2 , CL 2 , BF 2 , BE 2 , and BR 2 , and those including 0.2 mm ground samples, CLSA 0.2 , SA 0.2 , CL 0.2 , BF 0.2 , BE 0.2 , and BR 0.2 .

Carbon and nitrogen analyses

The conventional determination of soil total carbon (Ct) and nitrogen (Nt) contents was carried out on finely ground (< 0.2 mm) and oven-dried (40°C during 24 h) aliquots by dry combustion using an Elemental Analyzer CHN Fisons / Carlo Erba NA 2000 (Milan, Italy). In the absence of carbonates, all carbon was assumed to be organic.

NIRS analysis

NIRS measurement

NIRS analysis uses empirical models to predict Ct and Nt from spectral data. The reflectance of the soil samples was determined in the near infrared region between 1100 and 2500 nm at 2 nm intervals using a Foss NIRSystems 5000 spectrophotometer (Silver Spring, MD, USA).

The use of this spectral region resulted from the instrument specifications, and was relevant according to [START_REF] Chang | Influence of soil moisture on near-infrared reflectance spectroscopic measurements of soil properties[END_REF], who achieved more accurate predictions of soil properties using the 1100-2500 nm than the 400-2500 nm region. Samples (ca. 5 g) were scanned using a ring cup, which is a 5 cm circular capsule with a quartz window, sealed at the back with cardboard. The ring cup was fixed within a rectangular transport cup.

Each sample spectrum, averaged from 15 spectra, was recorded as the logarithm of the inverse of the 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 four consecutive spectral points [START_REF] Sørensen | Determination of clay and other soil properties by near infrared spectroscopy[END_REF], yielding 173 data points per spectra (Shenk andWesterhaus, 1991b, and[START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[END_REF][START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[END_REF], kept every fifth point).

Spectrum preprocessing

Derivatives were used to reduce baseline variation and enhance spectral features (Reeves et al., 2002). They were calculated over a 4 point gap with 4 point smoothing. Both first derivatives (144) and second derivatives (244) were evaluated in conjunction with either a standard normal variate transform (SNV, mean centring and variance scaling), to reduce the particle size effect, or a standard normal variate transform with detrending (SNVD), which additionally removes the linear or curvilinear trend of each spectrum [START_REF] Barnes | Standard normal variate transformation and detrending of near-infrared diffuse reflectance spectra[END_REF]. This gave a total of four preprocessing methods: SNV 144, SNVD 144, SNV 244, and SNVD 244.

Identifying spectral outliers

For each of the sample sets under study and each of the four preprocessing methods, a principal component analysis (PCA) was carried out on the complete set of sample spectra for the calculation of the Mahalanobis distance H [START_REF] Mark | Qualitative near-infrared reflectance analysis[END_REF]. The H statistic provides quantitative information on how sample spectra deviate from the mean sample spectrum. Samples with H > 3 were considered spectral outliers and were eliminated from further investigations (Shenk and Westerhaus, 1991a). Depending on the sample set, preparation and preprocessing method, the number of spectral outliers ranged from 0 to 7, representing less than 5% of the set population (less than 3% in 41 out of the 48 set  preparation  preprocessing combinations).

Calibration

After eliminating the spectral outliers, modified partial least square regression (MPLS; Shenk and Westerhaus, 1991b) was used to relate the spectra with the measured Ct and Nt. The MPLS regression combines PCA and multiple regression in order to reduce a complex spectral matrix to 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].

Each sample set was divided into a calibration subset and a remaining validation subset. The calibration subset of large sets (CLSA, SA) included 100 samples, whereas that of smaller sets (CL, BF, BE, BR) included 70% of the samples. The calibration subset was selected by the software to include the most representative samples of the set (i.e. closest neighbour samples, determined by Euclidian distances between spectra, were represented by one sample only). The literature has often reported random selection of calibration subsets [START_REF] Ludwig | Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability[END_REF][START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[END_REF][START_REF] Móron | Determination of potentially mineralizable nitrogen and nitrogen in particulate organic matter fractions in soil by visible and near-infrared reflectance spectroscopy[END_REF]) but it did not seem appropriate in this study as different random selections within the same set sometimes led to very different results. Cross validation was performed on the calibration set to determine the optimum number of terms to be included in the model (using too many factors may cause overfitting, which reduces the validation performance). The calibration set was divided into six groups, five being used for developing the model and one for prediction. The procedure was performed six times to use all samples for both model development and prediction. The residuals of the six predictions were pooled to calculate the standard error of cross validation between predicted and measured values (SECV). The outliers for calibration (i.e. samples with t > 2.5) were removed and another cross validation was performed, the procedure being carried out twice, and then all remaining calibration samples were used to calculate the final model. The number of factors giving the lowest final SECV determined the optimal number of terms to be used for the calibration. The performance of the calibration model was assessed using the coefficient of determination (R²) and the ratio of performance to deviation (RPD), which is the ratio of standard deviation to SECV. Though RPD > 3 has been recommended for NIRS applications in agriculture, forestry and food sciences [START_REF] Williams | Variables affecting near infrared reflectance spectroscopic analysis[END_REF][START_REF] Baillères | Near infrared analysis as a tool for rapid screening of some major wood characteristics in a eucalyptus breeding program[END_REF], the model accuracy has been considered acceptable in soil studies when RPD > 2 [START_REF] Chang | Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[END_REF][START_REF] Brown | Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana[END_REF].

Validation

The prediction accuracy of the model was evaluated on the validation subset (which had not been used for model development), using the validation R² and standard error of prediction between predicted and measured values (SEP). According to [START_REF] Morra | Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy[END_REF], the performance of prediction equations was also evaluated using the coefficient of variation (CV, in %), calculated as the ratio of SEP to the mean reference value. In addition to this validation CV, the calibration CV was defined as the ratio of SECV to the mean reference value.

Results and discussion

Reference data

Conventional determination results (CHN) of Ct and Nt are presented in Table 2 (the table

does not consider individually the sets including a number of samples that was too small to allow modelling, i.e. Senegal and Congo). The samples under study represented a wide range of Ct and Nt, with an overall variation from 1.7 to 42.4 g C kg -1 , and from 0.01 to 2.30 g N kg -1 , respectively. Coefficient of determination (R²) between Ct and Nt was 0.90 for CLSA, 0.70 for SA, 0.58 for CL, 0.88 for BF, 0.80 for BE, and 0.58 for BR (Nt was not determined on the samples from Congo). The data showed that Ct and Nt were much higher in clayey than in coarse-textured soils. The ratio of standard deviation to mean was higher within the total set of samples (88%) than within texture or localised subsets (29% to 59%). It was also higher within coarse-textured subsets than within clayey ones (33% to 59% vs. 29% to 33%).

Effect of the spectrum preprocessing

Four preprocessing methods were applied to the spectra, in order to compare the scatter corrections SNV and SNVD, and the first and second derivatives. Considering each of the twelve sample sets (i.e. CLSA 0.2 , CLSA 2 , SA 0.2 , SA 2 , CL 0.2 , CL 2 , BF 0.2 , BF 2 , BE 0.2 , BE 2 , BR 0.2 and BR 2 ), SNVD 144, and to a lesser extent, SNV 144, often gave the most accurate calibration (i.e. low SECV, high calibration R² and RPD) and rarely gave poor calibrations, whereas SNV 244 and SNVD 244 often gave poor calibrations (Tables 3 and4 for Ct, Tables 5 and6 for Nt). Moreover, SNV 144 clearly gave the highest number of accurate predictions (lowest SEP and highest validation R² for eight and nine out of the twelve sets for Ct and Nt, respectively) and the lowest number of poor predictions, though it was sometimes outperformed (e.g. regarding Ct, by SNV 244 for BE 2 and by SNVD 144 for CL 2 and BR 2 ).

Overall, SNV 144 proved to be the most appropriate preprocessing method for Ct and Nt prediction, especially when clayey sets were ground. Most of the data presented below was obtained using this preprocessing method.

While SNV transformation has been used by some authors for soil Ct (or organic carbon content, Corg) and Nt prediction [START_REF] Reeves | The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils[END_REF], SNVD seems to be more frequently used [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] Ludwig | Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability[END_REF][START_REF] Coûteaux | Near infrared reflectance spectroscopy for determination of organic matter fractions including microbial biomass in coniferous forest soils[END_REF]Morón and Cozzolino, 2004).

However, many authors tested several preprocessing methods and selected the most suitable for each property [START_REF] Reeves | Quantitative analysis of agricultural soils using near infrared reflectance spectroscopy and a fibre-optic probe[END_REF][START_REF] Mccarty | Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement[END_REF][START_REF] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF][START_REF] Sørensen | Determination of clay and other soil properties by near infrared spectroscopy[END_REF]. Moreover, some other authors have reported that normalisation did not improve the calibration [START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[END_REF]. First derivatives of the spectra have frequently been used for Ct, Corg and Nt prediction [START_REF] Chang | Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[END_REF][START_REF] Reeves | The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils[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] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF]Morón and Cozzolino, 2004) but second derivatives could sometimes result in more accurate results [START_REF] Salgó | Characterisation of soils by the near infrared technique[END_REF][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].

Nevertheless, studying 2 mm sieved topsoil samples that were mostly coarse-textured, [START_REF] Sørensen | Determination of clay and other soil properties by near infrared spectroscopy[END_REF] found that the accuracy of Ct prediction was relatively insensitive to the preprocessing method. This does not agree with the results for SA 2 , BF 2 and BE 2 .

Measured vs. predicted values of Ct and Nt

For both CLSA 0.2 and CLSA 2 the comparison between measured and predicted values of Ct, using SNV 144, shows two distinct subpopulations (Figure 1): one represented low carbon, coarse-textured soils and the other, high carbon, clayey soils, among which numerous samples were poorly predicted. The difference in spatial pattern of measured vs. predicted Ct between the complete set (CLSA) and the two texture sets (SA and CL) indicates that the corresponding calibration models differed markedly. This probably related to the strong difference in soil properties between clayey and coarse-texture soils, beyond the difference in Ct. Figure 1 also shows the presence of a very poorly predicted sample for BE 2 , and a trend to under-predict some of the highest Ct values for CLSA 0.2 , SA 0.2 , BF 0.2 , BF 2 , BE 2 , and BR 2 .

Figure 2 similarly presents measured vs. predicted values of Nt for the twelve sample sets, using SNV 144. Unlike the results for Ct, there was no clear division of CLSA into two subpopulations, and the tendency to under-predict relatively high values was less frequent.

Figure 2 shows, however, that several values were markedly under-predicted for BF 0.2 and BE 0.2 (localised coarse-textured sets).

On the whole, the accuracy of NIRS prediction of Ct and Nt was satisfactory. Using SNV 144, validation R² ranged from 0.77 to 0.96 for Ct, except for BE 2 (0.55; but 0.76 with SNV 244), and from 0.63 to 0.90 for Nt. For texture sets (SA and CL) and localised sets (BF, BE, and BR), validation CV ranged from 7% to 18% for Ct, and from 8% to 25% for Nt, whereas it was 31% to 35% for the total set (CLSA). Prediction of Ct and Nt for the set collected in Benin was often less accurate than for the other sets. This could be explained by the relative heterogeneity of this set, which originated from a small area but included a wide range of textures due to the sharp increase in clay content with depth (< 10% in the topsoil but up to 40% at a 50 cm depth, the sand content always being more than 45%).

Effect of sample preparation

The effect of sample preparation on calibration accuracy was not clear for Ct (Tables 3 and4).

Compared to 2 mm sieving, 0.2 mm grinding resulted in a more accurate calibration for CLSA (lower SECV, higher R²) but in a poorer calibration for BE, whereas similar calibrations with ground and sieved samples were recorded for the other sets. However, grinding generally gave more accurate Ct prediction than sieving: grinding always yielded higher validation R² than sieving (for localised sets especially), and yielded lower SEP for five out of the six sets (CL, BE and BR especially), but not for BF (homogeneous, sandy). Grinding, therefore, seems preferable to sieving for NIRS prediction of Ct.

In contrast, regarding Nt, grinding gave more accurate calibrations than sieving for five out of the six sets (BE and BF especially), but not for CL (Tables 5 and6). Moreover, validation accuracy for Nt was not clearly affected by the sample preparation: it was greater with ground samples for two sets, greater with sieved samples for two sets and for two sets there was little difference between sieving and grinding. Nevertheless, grinding clearly improved the accuracy of Nt prediction for BR (homogeneous, clayey) and reduced it for BF (homogeneous, sandy).

Comparisons of NIRS prediction carried out on ground and sieved samples have been reported in several papers. For Australian Vertisols (0-60 cm soil layers, 40-75% clay), [START_REF] Dalal | Simultaneous determination of moisture, organic carbon and total nitrogen by near infrared reflectance spectrophotometry[END_REF] observed more accurate prediction of Corg and Nt with ground (< 0.25 mm) than with sieved (< 2 mm) samples: validation CV for Corg was ca. 15% and 25% for ground and sieved samples, respectively. [START_REF] Reeves | The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils[END_REF] similarly reported more accurate calibration for Ct with ground than with sieved topsoil samples collected from a 20-ha field: calibration R² and CV were 0.93 vs. 0.78, and 7% vs. 13%, respectively. However, soil type, texture, and particle-size of ground and sieved samples were not specified. For a set of 0-10 cm samples collected on Sodosols, Chromosols and Vertisols within the same region, [START_REF] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF] also reported more accurate Ct and Nt prediction after grinding than after sieving: validation CV was 8% vs. 10% for Ct, and 11% vs. 15% for Nt, respectively. In contrast, [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], studying sandy soils from three regions in Norway (from 59°N to 67°N), found better prediction of Corg and Nt for sieved (< 4 mm) than for ground (< 0.5 mm) samples. Gathering literature data would thus indicate that grinding samples results in more accurate NIRS prediction than sieving for clayey or localised sets, but in worse prediction for a sandy set originating from various locations. This is consistent with the results of this study, which showed that the effect of grinding on prediction accuracy was very positive for localised clayey sets, moderately positive for heterogeneous sets (for Ct), and unclear for localised coarse-textured sets. Considering a wide range of soils that could be divided in different subsets, the present study thus helped confirming deductions drawn from the gathering of studies carried out on narrow ranges of soils.

The effect of soil texture on the usefulness of sample grinding may be attributed to macroaggregates and coarse sands. [START_REF] Chang | Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[END_REF] mentioned that size and arrangement of particles could affect the path of light transmission. Indeed, it is likely that the reflectance of clayey samples is influenced by their content in macro-aggregates and that this effect is reduced by grinding. Thus, grinding reduces aggregate influence on reflectance in clayey samples. However, sandy soils include few aggregates, thus 0.2 mm grinding mainly results in the breaking up of coarse sands, and, owing to their crystalline nature (sands were mainly constituted of quartz in the soils under study), in the creation of new planes that can disturb light reflection. This may help explain why grinding was not useful for sandy samples.

However, it seems preferable always to use the same sample preparation, especially when studying sets that include a wide diversity of soils, and so grinding might be recommended.

Effect of set heterogeneity

Regarding Ct, the heterogeneity of sample sets had a strong influence on calibration and validation statistics (Table 3 and4, andFigure 1). This effect was assessed by studying changes in R², RPD and CV (ratio of SECV or SEP to mean) with set heterogeneity. Changes in SECV or SEP were not taken into consideration because these parameters depend on Ct range (e.g. comparing SECV values of 0.6 and 2.1 g C kg -1 is not relevant when the respective Ct ranges are 1.7-4.6 and 10.8-22.3 g C kg -1 ). Calibration CV generally decreased (data not shown) and calibration R² and RPD increased as set homogeneity increased (i.e. from CLSA to SA and CL, and from SA to BF and BE; but not from CL to BR). The quality of calibration statistics for Ct thus increased with set homogeneity in general, except for the Brazilian clayey soils. Moreover, the accuracy of Ct prediction was clearly higher in the texture sets than in the total set: validation CV was markedly higher for SA and CL than for CLSA, while differences in validation R² were slight. For ground samples, prediction accuracy for Ct was also higher in the localised sets than in the texture sets: validation CV was slightly lower and validation R² generally higher for BF 0.2 and BE 0.2 than for SA 0.2 and for BR 0.2 than for CL 0.2 .

For sieved samples, the difference between localised sets and texture sets was less clear: the prediction accuracy was also higher for BF 2 than for SA 2 , but it was lower for BE 2 than for SA 2 and lower for BR 2 than for CL 2 . In conclusion, the accuracy of Ct prediction was clearly improved when the samples were separated into texture sets (i.e. clayey vs. coarse-textured soils). For ground samples the prediction was further improved by separation into localised sets. However, for sieved samples, separation into localised sets only improved the prediction for the sandy set.

In contrast, the effect of set heterogeneity on the calibration accuracy for Nt was unclear.

Higher set homogeneity generally resulted in a lower calibration CV (i.e. from CLSA to SA then to BF, and from CLSA to CL then to BR; but not from SA to BE). However, greater homogeneity did not always result in higher calibration R² (e.g. R² was lower for BF than for SA and CLSA). The effect of set heterogeneity on the validation accuracy for Nt was also unclear. Higher set homogeneity resulted in a lower validation CV (i.e. from CLSA to SA and CL, from SA to BF and BE, and from CL to BR) but also often in lower validation R² (e.g. from CLSA to SA and CL). Set homogeneity did not clearly affect Nt prediction on the whole, though prediction accuracy was greater for the localised clayey set (BR) and for the sieved localised sandy set (BF 2 ) than for the corresponding heterogeneous sets (CL and CLSA, and SA 2 and CLSA 2 , respectively).

Higher validation CV and lower validation R² for heterogeneous than for homogeneous sets could be deduced from comparison between studies that included either homogeneous or heterogeneous sets (Figure 3; in this figure, set heterogeneity was expressed by the difference between maximum and minimum clay content, i.e. clay range). This was corroborated by studies including sets of variable heterogeneity, though heterogeneity rarely related to soil type: for Ct (or Corg) and Nt, validation CV was higher for whole Vertisol profiles than for the more homogeneous topsoil layers (30-40% vs. 15-25%; [START_REF] Dalal | Simultaneous determination of moisture, organic carbon and total nitrogen by near infrared reflectance spectrophotometry[END_REF]) and higher for silt plus clay fractions than for silt fractions alone (19% vs. 14-17%; [START_REF] Morra | Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy[END_REF]. On the whole, the literature suggests that prediction of Ct (or Corg) and Nt was less accurate for sets including a wide diversity of soils than for those including narrower ranges of soil types: validation CV and R² were 25%-45% vs. 6%-30%, and 0.55-0.82 vs. 0.85-0.99, respectively. For Ct this was confirmed by the results of the present study, specifically addressing the effect of soil range diversity. More accurate prediction for homogeneous than for heterogeneous sets implied that robust calibrations might be difficult to achieve, as reported by several authors [START_REF] Sudduth | Geographic operating range evaluation of a NIR soil sensor[END_REF][START_REF] Brown | Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana[END_REF][START_REF] Sørensen | Determination of clay and other soil properties by near infrared spectroscopy[END_REF]. Weak relationship between soil organic matter content and reflectance when soil samples are collected from large geographic areas has been attributed to probable parent-material influences on soil mineral reflectance [START_REF] Henderson | High dimensional reflectance analysis of soil organic matter[END_REF].

However, the results of this study suggest that Nt prediction was not clearly affected by set heterogeneity: increasing set homogeneity generally decreased both validation CV and R².

Although this was not confirmed in the literature (cf. Figure 3), several papers suggested that Nt prediction is less clearly affected by set homogeneity than Ct prediction. [START_REF] Morra | Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy[END_REF] observed that the increase in set homogeneity resulted in a greater decrease in validation CV for Ct than for Nt, and [START_REF] Dalal | Simultaneous determination of moisture, organic carbon and total nitrogen by near infrared reflectance spectrophotometry[END_REF] showed a similar result for sieved samples.

Comparison of prediction accuracy for Ct and Nt

Using SNV 144, the calibration accuracy was, in general, higher for Ct than for Nt except for CLSA 2 (similar accuracy for Ct and Nt) and BR 2 (higher R² and RPD for Nt). For the ground sets, the validation accuracy was also higher in general for Ct than for Nt: Ct prediction was more accurate than Nt prediction for four out of the six ground sets, less accurate for CLSA 0.2 and there was no clear difference for BR 0.2 . For sieved sets, however, the validation accuracy for Ct and Nt did not differ clearly: Ct prediction was more accurate for one set (SA 2 ), Nt prediction was more accurate for two sets (CLSA 2 and BE 2 ), and the accuracy of the Ct and Nt predictions was similar for the three other sets. To summarise: (i) of the sets where Ct prediction was more accurate than the Nt prediction, most were ground sets and (ii) prediction for the total set (CLSA) tended to be more accurate for Nt than for Ct. Also grinding and set homogeneity had a generally positive effect on Ct prediction, but no clear effect on Nt prediction.

Studying ground samples from Australian Vertisols (0-60 cm layers), [START_REF] Dalal | Simultaneous determination of moisture, organic carbon and total nitrogen by near infrared reflectance spectrophotometry[END_REF] found similar validation R² and CV for Corg and Nt (0.85-0.86, and ca. 15%, respectively).

For a fairly homogeneous set of ground fine-textured samples (silty loam Ultisols), [START_REF] Reeves | Quantitative analysis of agricultural soils using near infrared reflectance spectroscopy and a fibre-optic probe[END_REF] also found similar calibration R² and CV for Corg and Nt (0.97 and 6%, respectively). For a more heterogeneous set of ground sandy samples originating from different regions of Norway (59°N to 67°N), data from [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] showed that validation CV was similar for Corg and Nt but that R² was higher for Corg. For a fairly heterogeneous set of ground topsoil samples (three soil types within the same Australian region), data from [START_REF] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF] also showed a validation CV lower for Ct than for Nt (8% vs. 11%, respectively). Thus data from the literature confirmed that for ground samples, Ct and Nt prediction accuracy was similar for homogeneous fine-textured sets (cf. BR 0.2 ), but that it was more accurate for Ct than for Nt for moderately heterogeneous or for coarse-textured sets (cf. SA 0.2, CL 0.2 , BF 0.2 and BE 0.2 ).

For sieved samples, data from [START_REF] Dalal | Simultaneous determination of moisture, organic carbon and total nitrogen by near infrared reflectance spectrophotometry[END_REF] for Australian Vertisols showed similar validation CV for Corg and Nt for topsoil (0-60 cm) samples (ca. 25%), but higher validation CV for Corg than for Nt for whole profiles (i.e. less homogeneous: ca. 40% vs. 35%, respectively). However, data from [START_REF] Coûteaux | Near infrared reflectance spectroscopy for determination of organic matter fractions including microbial biomass in coniferous forest soils[END_REF] for a fairly homogeneous set indicated that prediction accuracy was greater for Ct than for Nt (validation R² and CV were 0.99 vs. 0.98 and 6% vs. 10%, respectively). Similarly, data from [START_REF] Salgó | Characterisation of soils by the near infrared technique[END_REF][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] and [START_REF] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF], for heterogeneous sample sets, showed more accurate prediction of Ct (or Corg) than Nt. Considering literature data as well as results from this study, it was thus difficult to find a clear trend when comparing prediction accuracy for Ct (or Corg) and Nt using sieved samples.

It has been reported that NIRS determination of Nt might be indirect, due to close correlation with Ct, rather than direct, due to specific N absorbers [START_REF] Martin | Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy[END_REF]. In the present study, prediction accuracy was not always higher for Ct than for Nt (e.g. CLSA 2 and BR 2 ), and correlation between Nt and Ct was rather loose within several sets (R²  0.70 for SA, CL and BR). Thus, from a general viewpoint, it could not be concluded that NIRS determination of Nt was indirect and resulted from NIRS determination of Ct and correlation between Ct and Nt. This confirmed that NIRS is able to determine Nt directly, as reported by [START_REF] Chang | Near-infrared reflectance spectroscopic analysis of soil C and N[END_REF]. However, for BF 2 , much lower calibration R² for Nt than for Ct (0.69 vs. 0.93) and close correlation between Nt and Ct (R² = 0.88) suggested that NIRS prediction of Nt might be indirect. The question also arose for BF 0.2 , but to a lesser extent. Following [START_REF] Martin | Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy[END_REF], it could be hypothesized that depending on the studied set, Nt was predicted best on its correlation with Ct when the correlation was high, but directly otherwise. In the present study, data strongly suggested that NIRS determination of Nt was direct for most sets, but that it might be indirect for some few other ones (i.e. BF, which was the most sandy set).

Conclusion

This study, carried out on a range of low activity clay soils, demonstrated the usefulness of NIRS to determine Ct and Nt in tropical soils. The most accurate prediction of Ct and Nt was generally achieved using scatter correction without detrending and first derivatives (preprocessing method SNV 144). Soil preparation had a clear effect on Ct prediction accuracy. This was, in general, higher for 0.2 mm ground samples than for 2 mm sieved samples, although grinding was less beneficial for sandy sets. The effect of soil preparation on Nt prediction was less clear in general, however grinding increased prediction accuracy for localised clayey sets and decreased it for localised sandy ones. On the whole, sample grinding could be recommended, as it yielded an overall improvement in the accuracy of Ct and Nt prediction. Moreover, set homogeneity affected the accuracy of Ct prediction, which was clearly higher when clayey and coarse-textured sets were analysed separately. For ground samples, the accuracy of Ct prediction further increased when localised sets were analysed. This was not true for sieved samples. Set homogeneity had less effect on Nt prediction, the accuracy was not improved by analysing texture sets separately, nevertheless it was often higher for homogeneous localised sets. On the whole, the study of homogeneous sample sets could be recommended for Ct and Nt prediction, suggesting that NIRS is particularly appropriate for localised studies. Finally, the present study confirmed deductions that could be drawn from comparisons between earlier studies (e.g. studies on clayey soils vs. studies on sandy soils, studies on homogeneous sets vs. studies on heterogeneous sets), and helped optimize the conditions for NIRS determination of soil properties. SECV and SEP are standard error of cross validation and of prediction, respectively. RPD is the ratio of performance to deviation, i.e. the ratio of standard deviation to SECV. CV is the coefficient of variation, defined as the ratio of SEP to the mean. SECV and SEP are standard error of cross validation and of prediction, respectively. RPD is the ratio of performance to deviation, i.e. the ratio of standard deviation to SECV. CV is the coefficient of variation, i.e. the ratio of SEP to the mean. SA and CL are the coarse-textured and the clayey subset, respectively. SECV and SEP are standard error of cross validation and of prediction, respectively. RPD is the ratio of performance to deviation, i.e. the ratio of standard deviation to SECV. CV is the coefficient of variation, i.e. the ratio of SEP to the mean. SECV and SEP are standard error of cross validation and of prediction, respectively. RPD is the ratio of performance to deviation, i.e. the ratio of standard deviation to SECV. CV is the coefficient of variation, i.e. the ratio of SEP to the mean. SA and CL are the coarse-textured and the clayey subset, respectively. Comparison of data from this study and from the literature (References: [START_REF] Dalal | Simultaneous determination of moisture, organic carbon and total nitrogen by near infrared reflectance spectrophotometry[END_REF]Henry (1986), Ben-Dor and[START_REF] Ben-Dor | Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[END_REF], [START_REF] Chang | Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[END_REF], [START_REF] Reeves | Quantitative analysis of agricultural soils using near infrared reflectance spectroscopy and a fibre-optic probe[END_REF], [START_REF] Chang | Near-infrared reflectance spectroscopic analysis of soil C and N[END_REF], [START_REF] Ludwig | Near infrared spectroscopy of forest soils to determine chemical and biological properties related to soil sustainability[END_REF]), McCarty et al. (2002), [START_REF] Shepherd | Development of reflectance spectral librairies for characterization of soil properties[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] Russell | Sample preparation and prediction of soil organic matter properties by near infrared reflectance spectroscopy[END_REF], and [START_REF] Sørensen | Determination of clay and other soil properties by near infrared spectroscopy[END_REF]; for most of these studies, clay range was estimated). Clay range (g 100 g -1 ) Nt

Figure 1 .Figure 2 .Figure 3 .

 123 Figure1. Comparison of measured and predicted Ct values for validation subsets from the total set (CLSA), the coarse-textured set (SA), the clayey set (CL), Burkina Faso (BF), Benin (BE), and Brazil (BR), using 0.2 mm ground or 2 mm sieved soil samples (with SNV 144).

Table 1 .

 1 Presentation of the studied samples: location, soil type, texture, climate, land use, and depth.

	Location	Soil type a Soil texture	Mean annual	Management	Number of
			and clay	rainfall and		samples and
			content	temperature		depth
	Sonkorong,	Lixisol	Sandy	700 mm yr -1	Fallow	4 samples
	Senegal		ca. 12%	30°C	(shrub	0-10 cm
	13°45'N,				savanna)
	15°40'W
	Saria,	Kanhaplic	Sandy to	800 mm yr -1	Sorghum	79 samples
	Burkina Faso	Haplustalf, sandy clay	28°C		from 0-10
	12°16'N,	Ferric	loam			to 30-40 cm
	02°09'W	Acrisol	9-32%
	Agonkanmey,	Typic	Sandy to	1200 mm yr -1 Maize-based 116 samples
	Benin	Kandiustult, clay loam	27°C	cropping	from 0-5
	06°24'N,	Dystric	6-40%		systems	to 55-60 cm
	02°20'E	Nitisol
	Loudima,	Typic	Clayey	1100 mm yr -1 Savanna and	11 samples
	Congo	Haplorthox,	52-76%	25°C	cassava-based	0-10 and
	04°00'S,	Orthic			cropping	10-20 cm
	13°30'E	Ferralsol			systems
	Brasilia,	Typic	Clayey	1400 mm yr -1	Pastures,	39 samples
	Brazil (DF)	Haplustox,	44-79%	22°C	savanna	from 0-10
	15°50'S,	Orthic			and forest	to 50-70 cm
	47°50'W	Ferralsol
	Goiânia,	Typic	Clayey	1500 mm yr -1 Rice-soybean	8 samples
	Brazil (GO)	Haplustox,	43-46%	23°C	rotation with	0-10 and
	16°29'S,	Orthic			possible cover	10-30 cm
	49°19'W	Ferralsol			crop
	Pradópolis,	Typic	Clayey	1600 mm yr -1	Sugarcane	40 samples
	Brazil (SP)	Hapludox,	66-80%	23°C	and forest	0-5 and
	21°22'S,	Orthic				5-10 cm
	48°03'W	Ferralsol
	a Soil Survey Staff (1999) and/or FAO-ISRIC-ISSS (1998).

Table 2 .

 2 Conventional determination (CHN) of Ct and Nt: minimum, maximum, mean, and standard deviation (SD) for the total set, the texture subsets, and the localised subsets; n is the number of samples.

	Set	Ct (g C kg -1 )	Nt (g N kg -1 )
		n Min Max Mean SD	n Min Max Mean SD
	Total set (CLSA)	297 1.67 42.40 10.45 9.21 286 0.01 2.30 0.64 0.56
	Coarse-textured subset (SA)	199 1.67 9.85 4.62 1.82 199 0.01 0.76 0.32 0.15
	Clayey subset (CL)	98 10.80 42.40 22.28 6.40	87 0.40 2.30 1.38 0.45
	Burkina Faso (BF; coarse-textured)	79 1.67 9.85 4.26 1.73	79 0.16 0.73 0.36 0.12
	Benin (BE; coarse-textured)	116 1.72 9.72 4.83 1.87 116 0.01 0.76 0.29 0.17
	Brazil (BR; clayey)	87 10.80 42.40 22.19 6.35	87 0.40 2.30 1.38 0.45

Table 3 .

 3 Calibration and validation results for Ct depending on sample preparation (0.2 vs. 2 mm), set homogeneity (total set vs. texture subsets), and preprocessing method (first vs. second derivatives, i.e. 144 vs. 244, and with vs. without detrending, i.e. SNVD vs. SNV, respectively).

	Set	Preprocessing		Calibration set		Validation set
		method	n 1	SECV	R² RPD	n 2	SEP	R² CV
				(g C kg -1 )			(g C kg -1 )	(%)
	CLSA 0.2	SNV 144	91	2.73	0.89 3.0	191	3.54	0.84 33.9
	total set	SNVD 144	88	2.18	0.94 3.9	193	5.12	0.67 49.0
		SNV 244	91	2.87	0.86 2.7	191	3.48	0.85 33.3
		SNVD 244	89	2.77	0.86 2.7	191	3.65	0.84 34.9
	CLSA 2	SNV 144	92	3.33	0.87 2.8	193	3.69	0.81 35.3
	total set	SNVD 144	90	3.94	0.83 2.4	195	4.04	0.78 38.7
		SNV 244	87	3.36	0.84 2.5	197	5.38	0.62 51.5
		SNVD 244	88	3.41	0.83 2.5	197	5.52	0.61 52.8
	SA 0.2	SNV 144	95	0.64	0.89 3.0	92	0.61	0.86 13.2
	total coarse-SNVD 144	90	0.66	0.88 2.9	96	0.83	0.71 18.0
	textured	SNV 244	95	0.72	0.86 2.7	96	0.84	0.66 18.2
	subset	SNVD 244	95	0.72	0.86 2.7	96	0.84	0.66 18.2
	SA 2	SNV 144	93	0.65	0.91 3.3	93	0.66	0.81 14.3
	total coarse-SNVD 144	92	0.61	0.92 3.5	94	0.78	0.73 16.9
	textured	SNV 244	94	0.56	0.93 3.8	96	0.73	0.73 15.8
	subset	SNVD 244	94	0.57	0.93 3.7	96	0.73	0.73 15.8
	CL 0.2	SNV 144	64	2.09	0.91 3.4	28	1.68	0.84 7.5
	total clayey SNVD 144	59	2.16	0.85 2.6	25	3.14	0.75 14.1
	subset	SNV 244	66	2.84	0.74 2.0	26	2.30	0.86 10.3
		SNVD 244	65	2.59	0.85 2.6	26	2.71	0.79 12.2
	CL 2	SNV 144	63	2.06	0.88 2.9	26	2.11	0.80 9.5
	total clayey SNVD 144	64	2.04	0.89 3.0	28	1.86	0.89 8.3
	subset	SNV 244	67	2.57	0.78 2.1	26	3.00	0.64 13.5
		SNVD 244	68	2.86	0.77 2.1	26	3.01	0.65 13.5
	n 1 is the number of samples after the elimination of calibration outliers.
	n 2 is the number of samples used for the validation.

Table 4 .

 4 Calibration and validation results for Ct depending on sample preparation (0.2 vs. 2 mm), localisation (Burkina Faso BF, Benin BE, and Brazil BR), and preprocessing method (first vs. second derivatives, i.e. 144 vs. 244, and with vs. without detrending, i.e. SNVD vs. SNV, respectively).

	Set	Preprocessing		Calibration set		Validation set
		method	n 1 SECV	R² RPD n 2	SEP	R² CV
				(g C kg -1 )			(g C kg -1 )	(%)
	BF 0.2	SNV 144	48	0.38	0.92 3.6	22	0.44	0.96 10.3
	coarse-textured SNVD 144	48	0.42	0.89 3.0	22	0.44	0.94 10.3
	(included in SA) SNV 244	52	0.46	0.91 3.4	22	0.60	0.86 14.1
		SNVD 244	50	0.46	0.91 3.3	22	0.60	0.87 14.1
	BF 2	SNV 144	51	0.41	0.93 3.8	20	0.38	0.87 8.9
	coarse-textured SNVD 144	49	0.41	0.91 3.3	21	0.42	0.83 9.9
	(included in SA) SNV 244	49	0.37	0.94 4.2	22	0.43	0.84 10.1
		SNVD 244	49	0.41	0.93 3.9	22	0.46	0.84 10.8
	BE 0.2	SNV 144	78	0.63	0.91 3.4	33	0.50	0.78 10.4
	coarse-textured SNVD 144	74	0.54	0.93 3.9	34	0.78	0.59 16.1
	(included in SA) SNV 244	78	0.71	0.88 2.9	30	0.74	0.58 15.3
		SNVD 244	77	0.72	0.87 2.8	30	0.64	0.65 13.3
	BE 2	SNV 144	75	0.57	0.93 3.7	35	0.86	0.55 17.8
	coarse-textured SNVD 144	75	0.56	0.93 3.8	35	0.67	0.72 13.9
	(included in SA) SNV 244	74	0.63	0.91 3.3	35	0.62	0.76 12.8
		SNVD 244	73	0.58	0.93 3.6	35	0.63	0.75 13.0
	BR 0.2	SNV 144	53	2.49	0.86 2.6	25	1.50	0.89 6.8
	clayey	SNVD 144	52	2.24	0.89 3.0	25	2.84	0.59 12.8
	(included in CL) SNV 244	55	2.32	0.86 2.7	27	5.54	0.39 25.0
		SNVD 244	55	2.33	0.86 2.7	27	5.51	0.40 24.8
	BR 2	SNV 144	51	2.26	0.78 2.1	25	2.18	0.77 9.8
	clayey	SNVD 144	56	2.36	0.86 2.6	26	1.97	0.84 8.9
	(included in CL) SNV 244	58	2.35	0.85 2.6	27	2.70	0.69 12.2
		SNVD 244	58	2.54	0.85 2.6	27	2.06	0.81 9.3
	n 1 is the number of samples after the elimination of calibration outliers.
	n 2 is the number of samples used for the validation.

Table 5 .

 5 Calibration and validation results for Nt depending on sample preparation (0.2 vs. 2 mm), set homogeneity (total set vs. texture subsets), and preprocessing method (first vs. second derivatives, i.e. 144 vs. 244, and with vs. without detrending, i.e. SNVD vs. SNV, respectively).

	Set	Preprocessing		Calibration set		Validation set
		method	n 1	SECV	R² RPD	n 2	SEP	R² CV
				(g N kg -1 )			(g N kg -1 )	(%)
	CLSA 0.2	SNV 144	85	0.17	0.87 2.8	184	0.21	0.86 32.8
	total set	SNVD 144	85	0.20	0.85 2.5	188	0.33	0.67 51.6
		SNV 244	90	0.19	0.83 2.5	188	0.23	0.84 35.9
		SNVD 244	91	0.20	0.83 2.5	188	0.23	0.84 35.9
	CLSA 2	SNV 144	87	0.20	0.87 2.8	186	0.20	0.86 31.3
	total set	SNVD 144	88	0.26	0.81 2.3	187	0.24	0.79 37.5
		SNV 244	88	0.22	0.81 2.3	189	0.22	0.83 34.4
		SNVD 244	88	0.22	0.82 2.3	189	0.22	0.83 34.4
	SA 0.2	SNV 144	97	0.06	0.88 2.8	92	0.07	0.72 21.9
	total coarse-SNVD 144	94	0.05	0.89 2.4	96	0.07	0.70 21.9
	textured	SNV 244	95	0.07	0.84 2.4	96	0.08	0.59 25.0
	subset	SNVD 244	94	0.07	0.84 2.4	96	0.08	0.60 25.0
	SA 2	SNV 144	94	0.06	0.87 2.8	93	0.08	0.65 25.0
	total coarse-SNVD 144	95	0.07	0.84 2.6	94	0.07	0.67 21.9
	textured	SNV 244	97	0.07	0.83 2.6	96	0.07	0.68 21.9
	subset	SNVD 244	97	0.07	0.83 2.6	96	0.06	0.68 18.8
	CL 0.2	SNV 144	54	0.22	0.78 2.1	25	0.18	0.81 13.0
	total clayey SNVD 144	53	0.18	0.85 2.5	23	0.35	0.67 25.4
	subset	SNV 244	57	0.19	0.83 1.7	24	0.41	0.57 29.7
		SNVD 244	57	0.19	0.83 2.4	24	0.40	0.58 29.0
	CL 2	SNV 144	60	0.18	0.84 2.5	23	0.21	0.82 15.2
	total clayey SNVD 144	59	0.16	0.85 2.7	23	0.20	0.84 14.5
	Subset	SNV 244	60	0.16	0.88 2.9	23	0.23	0.73 16.7
		SNVD 244	60	0.16	0.88 2.9	23	0.23	0.72 16.7
	n 1 is the number of samples after the elimination of calibration outliers.
	n 2 is the number of samples used for the validation.

Table 6 .

 6 Calibration and validation results for Nt depending on sample preparation (0.2 vs. 2 mm), localisation (Burkina Faso BF, Benin BE, and Brazil BR), and preprocessing method (first vs. second derivatives, i.e. 144 vs. 244, and with vs. without detrending, i.e. SNVD vs. SNV, respectively). the number of samples after the elimination of calibration outliers. n 2 is the number of samples used for the validation.

	Set	Preprocessing		Calibration set		Validation set
		method	n 1 SECV	R² RPD	n 2	SEP	R² CV
				(g N kg -1 )			(g N kg -1 )	(%)
	BF 0.2	SNV 144	49	0.04	0.84 2.5	22	0.06	0.63 16.7
	coarse-textured SNVD 144	49	0.03	0.86 3.0	22	0.06	0.69 16.7
	(included in SA) SNV 244	50	0.04	0.85 2.5	22	0.05	0.76 13.9
		SNVD 244	50	0.04	0.86 2.5	22	0.05	0.75 13.9
	BF 2	SNV 144	48	0.05	0.69 1.8	20	0.03	0.87 8.3
	coarse-textured SNVD 144	49	0.04	0.89 3.0	21	0.05	0.62 13.9
	(included in SA) SNV 244	49	0.05	0.75 2.0	22	0.05	0.74 13.9
		SNVD 244	49	0.05	0.75 2.0	22	0.05	0.74 13.9
	BE 0.2	SNV 144	78	0.06	0.90 3.2	33	0.06	0.76 20.7
	coarse-textured SNVD 144	80	0.06	0.91 3.2	34	0.08	0.46 27.6
	(included in SA) SNV 244	78	0.07	0.87 2.7	30	0.08	0.28 27.6
		SNVD 244	78	0.07	0.87 2.7	30	0.08	0.29 27.6
	BE 2	SNV 144	77	0.07	0.87 2.7	35	0.05	0.66 17.2
	coarse-textured SNVD 144	78	0.07	0.87 2.7	35	0.07	0.63 24.1
	(included in SA) SNV 244	77	0.07	0.88 2.7	35	0.07	0.57 24.1
		SNVD 244	77	0.07	0.87 2.7	35	0.07	0.58 24.1
	BR 0.2	SNV 144	55	0.17	0.85 2.6	25	0.14	0.90 10.1
	clayey	SNVD 144	53	0.17	0.86 2.7	25	0.17	0.87 12.3
	(included in CL) SNV 244	54	0.19	0.82 2.4	27	0.39	0.60 28.3
		SNVD 244	54	0.19	0.82 2.4	27	0.38	0.61 27.5
	BR 2	SNV 144	55	0.18	0.84 2.5	25	0.18	0.85 13.0
	clayey	SNVD 144	58	0.18	0.84 2.5	26	0.21	0.79 15.2
	(included in CL) SNV 244	59	0.21	0.78 2.1	27	0.18	0.84 13.0
		SNVD 244	57	0.22	0.77 2.1	27	0.22	0.76 15.9
	n 1 is

Acknowledgments

We thank Anastase Azontonde, Lydie Chapuis-Lardy, Edgar De Luca, Edmond Hien, and Aurélie Metay for sample collection. We also thank anonymous reviewers for their helpful recommendations.


Keywords: Connectivity, green and blue infrastructure, landscape fragmentation, riparian areas, urban planning

La fragmentation du paysage est l'un des principaux facteurs qui affaiblissent encore les vestiges de végétation dans les zones d'expansion urbaine. L'application du concept de trame verte et bleue, qui comprend, entre autres, la création de couloirs verts dans les zones riveraines, est une stratégie qui peut atténuer ces impacts. Cet article a pour objectif de caractériser ce phénomène en analysant l'évolution de l'occupation urbaine dans deux bassins versants situés dans la région métropolitaine de Belo Horizonte, au Brésil, et en utilisant de métriques de paysage pour sa quantification. Vu que la fragmentation a augmentée entre 1985 et 2017, nous avons commencé par proposer des stratégies de gestion du paysage, en développant des scénarios d'occupation permettant d'accroître la connectivité de zones vertes, par le biais de la récupération de zones riveraines. Les résultats montrent qu'un scénario réaliste, qui considère la récupération des zones riveraines non urbanisées de manière dense, peut améliorer la connectivité des zones vertes à un niveau supérieur à celui de 1985.

Proposal for the use of riparian areas to mitigate the impact of urbanization in landscape fragmentation

Proposition d'utilisation des zones riveraines pour atténuer l'impact de l'urbanisation sur la fragmentation du paysage ROSA Deyvid*, NASCIMENTO Nilo*

INTRODUCTION

Landscape fragmentation is one of the principal impacts of urbanization [START_REF] Rogan | Impacts of Habitat Loss and Fragmentation on Terrestrial Biodiversity[END_REF]. Reduction and isolation of green areas in patches separated by a matrix of anthropogenic soil use seriously affect the structure and functions of these remnants, leading to biodiversity loss [START_REF] Haddad | Habitat fragmentation and its lasting impact on Earth's ecosystems[END_REF]. Reduction and fragmentation of natural areas in the urban environment are also related to several socio-environmental impacts such as reduced human access to green areas, increased surface runoff and consequent increase in flood occurrence, deterioration of air quality, increased surface temperature and further [START_REF] Di Giulio | Effects of habitat and landscape fragmentation on humans and biodiversity in densely populated landscapes[END_REF].

In this context, a strategy for urban space planning and management that seeks to mitigate the impacts of fragmentation is needed [START_REF] Forman | Nature conserved in changing landscapes with and without spatial planning[END_REF]. The application of the green and blue infrastructure concept, which includes the creation of green corridors in the riparian areas, can bring, in addition to ecological benefits, hydrological, sanitary, social, environmental and economic gains [START_REF] Nascimento | Green and blue infrastructure at metropolitan scale: a water sustainability approach in the Metropolitan Region of Belo Horizonte[END_REF]. The green and blue infrastructure was recently considered in the territorial planning of the metropolitan region of Belo Horizonte, Brazil, involving participatory processes in the construction of ecological corridors proposals in environmentally strategic areas [START_REF] Oliveira | The green and blue infrastrucure in territorial planning: approximations and distances[END_REF].

Thus, the objective of this study is to evaluate the impact of urban expansion on the fragmentation of the landscape and to propose alternative scenarios for the urbanization evolution, considering the need to mitigate the impacts of fragmentation, respecting the characteristics of the urban occupation, as well as the vocation of the studied areas. These scenarios will be constructed in order to extend the connectivity, according to the principles of green and blue infrastructure, through the creation of urban green corridors [START_REF] Pellegrino | The border landscape: a strategy for the management of waters, biodiversity and people[END_REF]. The proposed study is part of the MoMa-SE research project entitled "Modelling strategic metropolitan water sources as an input for water and land management in the face of climate change" and developed in partnership with the Federal University of Minas Gerais, University of Brasília, University of São Paulo and LEESU, a laboratory of the École de Ponts ParisTech, in France. This project proposes, as a main hypothesis, that it is possible to increase the resilience of strategic water sources to anthropic pressures and climatic changes, through measures of land use regulation, adoption of soil and water conservation techniques based in concepts of green and blue infrastructure. In Minas Gerais, three reservoirs with different uses and catchments with different levels of urbanization are under study: the reservoirs of Serra Azul, Vargem das Flores, and Pampulha. This paper focuses on the last two catchments.

METHOD

The characterization of land use and occupation evolution in the study area was made based on the comparison of satellite image classification for the years 1985 and 2017. This classification was obtained from the database Collection 3 of MapBiomas Project, which was based on images of Landsat satellite, with spatial resolution of 30 meters (Mapbiomas Project, 2018).

According to the Brazilian Forestry Code, the riparian areas are stated as permanent protection areas (APP), and extents to a minimum of 30 meters of the watercourses. APP, in addition to the primary protection functions for the watercourse itself and the riparian forest, may alleviate the effects of fragmentation by connecting isolated green patches [START_REF] Metzger | Potential impacts of proposed changes to the Brazilian Forest Code on biodiversity and ecosystem services[END_REF]. For this reason, they were used strategically in the construction of recovery scenarios in the present research. Two modeling scenarios were evaluated: scenario 2017Par, the same landscape of 2017, with recovery of APP that are not developed urban occupation; and the scenario 2017Tot, with recovery of all APPs. The fragmentation degree of each scenario was evaluated by applying the landscape metrics that measure the fragmentation, through the software Fragstats 4.2.1 [START_REF] Mcgarigal | A landscape index of ecological integrity to inform landscape conservation[END_REF]. The metrics used were: percentage of landscape (PLAND), number of patches (NP), largest patch index (LPI), effective mesh size (MESH) and mean of Euclidean distance of nearest neighbor (ENN_MN).

RESULTS

The land use classification of both catchments, which as neighbor catchments, in 1985 and 2017 is shown in Figures 1 and2. The results of scenario construction with partial and total recovery of APP, maintaining the same soil occupation of 2017 in the areas outside the APP, are presented in Figures 3 and4 respectively.

In 32 years, there was a remarkable increase of the urbanization and reduction of the vegetated areas in both catchments. In Pampulha catchment, an increase of 23.2% in the urban area is observed, while the green areas (forest and savanna) decreased by 21.4%. In Vargem das Flores catchment, the urban area grew by 114.5%, while green areas decreased by 6.3% in respect to it in 1985. Therefore, an increase in anthropic impact in the remaining natural environments of the study areas is observed, which suffer from real estate market pressure and the demand for new areas for urban expansion. In this way, it can be considered that this would be a scenario that is not feasible and very difficult to be achieved. On the other hand, Scenario 2017Par would be closer to a possible real occupation in the catchments, with recovery of APP that have not yet been occupied with urban infrastructure and are therefore more prone to recovery. Even so, the connectivity gain obtained with partial recovery of APP is remarkable, as shown in Table 1. Between 1985 and 2017, there was a reduction of the NP, LPI and MESH, and an increase of the metric ENN_MN in both catchments. This result indicates the increase in the fragments isolation (ENN_MN), a reduction of their areas (MESH) and the disappearance of smaller fragments (NP). Stands out the difference in the magnitude order in metrics LPI and MESH between the two catchments. Although the PLAND in Vargem das Flores is approximately three times greater than in Pampulha, the largest patch of the first remained almost ten times greater than the last. The difference between the weighted mean area of the patches is even more remarkable. While MESH in Vargem das Flores catchment remains greater than 1,100 hectares in 2017, in Pampulha catchment it did not exceed 22.2 hectares in 1985. Thus, the fragmentation between 1985 and 2017 increased.

With partial and total recovery of APP, a growth of 3.4 and 10.3 is obtained for PLAND in the Pampulha catchment, and a growth of 4.2 and 7.4 in PLAND for the Vargem das Flores catchment, respectively. That is, the recovery of degraded APP, even in the most optimistic scenario, reached a maximum percentage of 10%. The improvement in the degree of connectivity reached much more representative levels. The metrics LPI and MESH increased in the two catchments, both in relation to 2017 and 1985. That is, even the partial recovery of the APPs generated a reduction in fragmentation, including in relation to the 1985 scenario, with notable increase in the area occupied by the largest patch and the weighted mean area of the patches. On the other hand, the mean distance between patches decreased from 2017 to scenario 2017Par, that reached levels very close to that of 1985. Scenario 2017Tot, in turn, implied an even larger reduction of the ENN_MN, to lower values than that of 1985. The results of the evaluation of landscape fragmentation in the scenarios with partial and total recovery of the riparian areas show the potential of these areas for the promotion of connectivity. Highlight can be given to the ability of the more realistic scenario, with recovery only of non-consolidated occupation areas, to improve connectivity to even better levels than 32 years earlier.

In reference of stormwater management, an additional analysis can be done, considering that the riparian areas are usually the most impacted by floods. In this way, for the municipality of Belo Horizonte, the most urbanized one, in Pampulha catchment, the data from flood prone areas was crossed with the scenarios with recovery and protection of the riparian areas. The results of this crossing analysis are presented in Figure 5. [START_REF] Nascimento | Green and blue infrastructure at metropolitan scale: a water sustainability approach in the Metropolitan Region of Belo Horizonte[END_REF] It can be observed that 57.4% of flood prone areas are inserted in the riparian areas, the other 42.6% are, naturally, contiguous to these ones. In the actual urban land use law, there are restrictions to new occupations in flood prone areas, however few recommendations are available for flood management in consolidated occupation in these areas. Nevertheless, the existence of these flood prone areas, identified in the ambit of the Belo Horizonte urban water management plan, demands actions more effective than signaling (Figure 6) and the creation of escape routes. In this way, a different paradigm could be explored, with the restoration of riparian areas, particularly these ones prone to flood. This proposal had already been discussed previously in other studies, as illustrated by figures 7 and 8.

CONCLUSION

The proposed riparian area recovery, based on Brazilian environmental legislation, consisted of two possible paths: a complete recovery of permanent preservation areas marginal to watercourses, and a partial recovery, excluding areas with consolidated urban occupation. As expected, the scenario with full recovery would promote greater connectivity. However, the scenario with partial recovery of APP, which is more likely to be implemented, proved to promote considerable fragmentation reduction, and therefore is the recommended. In this sense, more attention should be given to flood prone areas, with the possible implementation of multifunctional linear parks, that could serve as green corridors and social interaction places with access restriction in rainy season. The implementation of this scenario, however, will require the efforts of public authorities, in order to encourage landowners to recover stretches of land that contain degraded riparian areas. The proposed scenario is aligned directly with the guidelines proposed by the Integrated Development Master Plan for the Metropolitan Region of Belo Horizonte, with the application of Green and Blue Network concepts, and has an intersection with other public policies in force and in operation in the State, such as the Payment for Environment Services.

Figure

  Figure 1. Land Use Map -1985 Figure 2. Land Use Map -2017

Figure

  Figure 5. Flood Prone Areas, Riparian Areas and Land Use Map -2017 Figure 6. Plate with flood warning in the Pampulha Catchment Figure 7. Actual scenario of Flor d'água Stream (Nascimento et al, 2016) Figure 8. Hypothetical scenario, with the stream restoration[START_REF] Nascimento | Green and blue infrastructure at metropolitan scale: a water sustainability approach in the Metropolitan Region of Belo Horizonte[END_REF]

Table 1 .

 1 Results of landscape metrics by scenario

	Metrics		Pampulha Basin 1985 2017 2017Par 2017Tot 1985 Vargem das Flores Basin 2017 2017Par 2017Tot
	NP	Number of patches	353	216	330	89	286	232	202	83
	LPI	Largest Patch Index	3.8	1.7	7.6	22.6	31.4	30.4	44.2	51.1
	MESH	Effective mesh size	22.2	9.8	60.9	505.0 1188.9 1143.7 2360.1 3110.8
	PLAND	Percentage of landscape 17.2 13.4	16.8	23.7	47.5	44.6	49.8	52.0
	ENN_MN Euclidean Nearest Neighbor 114.5 167.0 116.0	103.3	86.6	106.9	92.2	84.0

Acknowledgments

The authors would like to thank the research funding agencies FAPEMIG, CNPQ, CAPES and the National Water Agency of Brazil (ANA) for their financial support to the development of this research.