E Vaudour

C Gomez

Y Fouad

P Lagacherie

SENTINEL-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems

Keywords: soil properties, SENTINEL-2A, partial least squares regression, temperate soils, Mediterranean soils, agroecosystems

To be fully operational for facilitating decisions made at any spatial level, models and indicators of soil ecosystem functions require the use of precise spatially referenced soil information as inputs. This study aimed at exploring the capacity for SENTINEL-2A (S2A) multispectral satellite images to predict several topsoil properties in two contrasted pedoclimatic environments: a temperate region marked by intensive annual crop cultivation patterns and soils derived from loess or colluvium and/or marine limestone or chalk (Versailles Plain, 221 km 2 ); and a Mediterranean region marked by vineyard cultivation and soils derived from lacustrine limestone, calcareous sandstones, colluvium, or alluvial deposits (Peyne catchment, 48 km 2 ). Prediction models of soil properties based on partial least squares regressions (PLSR) were built from S2A spectra of 72 and 143 sampling locations across the Versailles Plain and Peyne catchment, respectively. Eight soil surface properties were investigated in both regions: pH, cation exchange capacity (CEC), texture fractions (Clay,

Introduction

Soils carry out a number of key environmental functions that are essential for human subsistence (e.g., food, fiber and timber production, water storage and redistribution, pollutant filtering and carbon storage). The understanding and modeling of physical, chemical and biological processes underlying these different functions have been the subject of many scientific works now that a number of models or indicators that represent these functions are available [START_REF] Sanchez | Environmental science. Digital soil map of the world[END_REF][START_REF] Adhikari | Linking soils to ecosystem services -a global review[END_REF][START_REF] Baveye | Soil "ecosystem" services and natural capital: critical appraisal of research on uncertain ground[END_REF]. To be fully operational in facilitating decisions made at local, national and global levels, these models and indicators require the use of precise spatially referenced soil information as inputs.

Given the lack of precision and extension of existing soil databases, Digital Soil Mapping (Mc Bratney et al., 2003) methods have been recognized as the most adequate and operational response to these requirements [START_REF] Arrouays | GlobalSoilMap: toward a fine-resolution global grid of soil properties[END_REF][START_REF] Minasny | Digital soil mapping: A brief history and some lessons[END_REF]. However, even though digital soil maps of soil properties are now being produced across the globe [START_REF] Arrouays | Digital soil mapping across the globe[END_REF] and for large areas-regions, countries and even continents-at high resolutions (100 m), they still provide very uncertain estimations of soil properties due to the limited availability of sites offering soil property measurements used for calibrating DSM models (Vaysse et al., 2015).

Visible, Near-infrared and Short-wave Infrared (VNIR/SWIR) hyperspectral remote sensing methods have been demonstrated to serve as promising means to significantly increase the number of sites offering quantitative estimations of topsoil properties (e.g., Soil Organic Carbon (SOC), pH, Cation Exchange Capacity (CEC), texture fractions (clay, silt, sand), Iron and Calcium Carbonate (CaCO3)) (e.g., Selige et al., 2008;[START_REF] Stevens | Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy[END_REF]Gomez et al., 2012;[START_REF] Vaudour | Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra[END_REF]. For bare soil surfaces, accurate local estimates (R²cv > 0.7) have been obtained for soil properties i) related to a chemical species that impacts soil surface reflectance values through absorption bands (e.g., OH-ion for clay) (Ben-Dor et al., 2002) or ii) highly correlated with the latter (e.g., CEC with clay content) (Ben-Dor et al., 2002), iii) showing a sufficient level of variability across study regions (Gomez et al., 2012a & b). For instance, [START_REF] Selige | High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures[END_REF] mapped SOC content, a soil property having specific spectral behaviour and high spatial variability over their study area. In addition, they also mapped Total Nitrogen, a soil property deprived of specific spectral features but, due to an associated chemical or physical structure, correlated to a soil property having such features (being SOC). DSM approaches are designed to employ this new source of soil data to allow for the exhaustive mapping of surface (Walker et al., 2017) and subsurface [START_REF] Lagacherie | Combining Vis-NIR hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia)[END_REF] properties.

Although hyperspectral VNIR/SWIR imagery has been proven to serve as a valuable tool for mapping key soil surface properties, it cannot be applied for large surface mapping or for temporal monitoring because it is expensive to apply and because hyperspectral VNIR/SWIR imaging data are not widely available. Indeed, only one hyperspectral VNIR/SWIR satellite sensor is currently operational. The HYPERION sensor offers a spatial resolution of 30 m, a spectral resolution of 10 nm and a swath of 7.5 km [START_REF] Folkman | EO1/Hyperion hyperspectral imager design, development, characterization and prediction[END_REF]. Other hyperspectral VNIR/SWIR imaging sensors are airborne sensors (e.g., the Hymap, AISA-DUAL and HySpex sensors) with spectral resolutions of 5 to 10 nm, spatial resolutions of approximately 4-5 m (depending to the flight altitude) and flight prints of generally less than 400 km² (depending to the studied case). Furthermore, few studies have obtained successful results in terms of mapping soil properties from VNIR-SWIR multispectral satellite imagery that allow one to capture a larger area from a single nearly instantaneous glance (e.g., [START_REF] Vaudour | Potential of SPOT multispectral satellite images for mapping topsoil organic carbon content over peri-urban croplands[END_REF][START_REF] Shabou | Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands[END_REF]. This can be attributed to i) the low revisit frequency of sensors, which compromises the acquisition of images under clear conditions, while time ranges of bare soil availability, and particularly for annual crop systems, are short and ii) the coarse spectral and spatial resolutions of most historical multispectral sensors, which limit prediction performance outcomes [START_REF] Gomez | Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using hyperspectral VNIR/SWIR imagery[END_REF][START_REF] Gomez | Predictive ability of soil properties to spectral degradation from laboratory Vis-NIR spectroscopy data[END_REF].

The recent release of multispectral satellite SENTINEL-2A data by the European Space Agency last November 2015 is revolutionary in developing images of large areas with a 290 km-swath width at high revisit frequencies (planned to be near-daily when the full satellite constellation is in orbit). The multispectral SENTINEL-2A satellite provides 13 spectral bands at spectral resolutions of between 20 and 180 nm and at spatial resolutions of 10 to 60 m ( §2.3).

The objective of this study is to assess the potential for SENTINEL-2A data to produce estimations of topsoil properties for large sets of sites. The study focuses on two French regions-the Versailles Plain and the Peyne Valley-that represent very different agroecosystems and that have both been previously used as test areas for topsoil mapping from VNIR/SWIR hyperspectral data [START_REF] Vaudour | Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra[END_REF]Gomez et al, 2012, respectively).

Materials

Study areas

Versailles Plain

The Versailles Plain is an agricultural peri-urban region of 221 km² located west of Paris (North of France) that is characterized by intensive annual crop cultivation across an area of ~99 km² (Fig. 1, left). The main crop rotations in the area involve winter wheat, winter rapeseed, winter and spring barley and maize on occasion [START_REF] Vaudour | Early-season mapping of crops and cultural operations using very high spatial resolution Pléiades images[END_REF]. Cultivation practices are mainly conventional, with early winter plowing applied at least 1 year out of every 3. The Versailles Plain is composed of two plateaus levels: one at a 120-m elevation in the center (lower limestone plateau) and another at a 170 m-elevation along the northern and southern edges of the area (upper millstone clay plateau). Both are oriented in the NW-SE direction toward the Palace of Versailles. Quaternary loessic deposits and loessic colluvium leave a mark on all landforms in the area and particularly on local plateaus, which have evolved into haplic or glossic luvisols according to the FAO classification (World Reference Base (WRB) [START_REF] Crahet | Soil Map of the Versailles Plain[END_REF][START_REF] Vaudour | Potential of SPOT multispectral satellite images for mapping topsoil organic carbon content over peri-urban croplands[END_REF]Vaudour et al., , 2014)). The largely forested upper plateau flanks are characterized by Fontainebleau acid sands that give rise to arenic cambisols, while the lower plateau flanks, which are largely cultivated, are composed of calcaric cambisols derived from limestone, colluvial materials and, along the lower slopes, chalk. Throughout the lower slopes and valleys, stagnic colluvic cambisols are derived from marls or alluviocolluvial materials.

Soil samples and ground observations

Soil sampling

This study gathered two soil data sets that were collected for the purpose of earlier studies (for Versailles, [START_REF] Vaudour | Potential of SPOT multispectral satellite images for mapping topsoil organic carbon content over peri-urban croplands[END_REF]Vaudour et al. , 2014a[START_REF] Vaudour | Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra[END_REF]for La Peyne, Gomez et al, 2012). Despite some differences in chemical analysis, it was found useful to gather them in view of enlarging the range of the tested pedological situations. A total of 72 and 143 topsoil samples were collected from the Versailles Plain and Peyne Valley, respectively (Fig. 1). For the Versailles Plain, 69 topsoil samples were collected from 2010 to 2013, and 13 topsoil samples were collected in 2016. All of the samples were composed of roughly 10 sub-samples collected to a depth of 8 cm from random locations within a 2.7 × 2.7 m square area centered at the sampling plot as recorded at its center using a Trimble Pathfinder®Power DGPS of 50 cm precision (Vaudour et al., 2014). From the Peyne Valley, 39 topsoil samples were collected in 2005, and 104 topsoil samples were collected in 2009. All of the samples were composed of five sub-samples collected to a depth of 5 cm from random locations within a 10 × 10 m square centered on the geographical position of the sampling plot as recorded by a Garmin GPS instrument.

Physico-chemical analysis

Topsoil samples collected from both study areas were air-dried and then crushed and sieved to 2 mm. Soil property determinations were performed through a classical physico-chemical analysis (Baize and Jabiol, 1995). In total, 8 common soil properties were considered for spectral modeling (Table 1): content of 3 particle sizes or granulometric fractions of 2 mmsieved fine earth (total silt, 2 -50 μm; total sand, 50 μm -2 mm and clay, < 2 µm); calcium carbonate (CaCO3) content; free iron content (Mehra-Jackson); soil organic carbon (SOC) content; pH; and cation exchange capacity (CEC).

Granulometric fractions (clay, silt, and sand) and CaCO3 content levels were measured from 143 soils samples collected from the Peyne Valley (Table 1). The three other soil properties (iron, CEC and pH) were measured from 104 soil samples collected from the Peyne Valley. For the Versailles Plain, all soil properties except for CEC (39 samples) were measured (Table 1).

Ground observations

For the Versailles Plain, soil surface conditions were observed on 15 and 17 March 2016 across 6 agricultural fields and for other fields planned for spectral measurements. For the Peyne Valley, vineyard soil surface conditions were observed on 24 March 2016 across the 0.9 km² sub-catchment of Roujan, which includes 75 viticultural fields. These observations enabled us to define the threshold of the vegetation index for retrieving bare soil pixels from S2A images. At the time that the images were acquired, the budburst stage had just begun for vines in the Peyne Valley, and hence vine vegetation had not yet developed. For the Versailles Plain, spring barley sowing had just started and maize and pea sowing had not started, hence allowing for the observation of bare soils used for spring crops. For both study areas, winter crops and grasslands were photosynthetically active and covering, and thus easily identifiable and removable by means of NDVI.

Remotely sensed images and geographic data

Two SENTINEL-2A (S2A) images were acquired under nearly clear conditions on 12 and 19

March 2016 from the Versailles Plain and Peyne Valley, respectively (Table 2). The multispectral instrument aboard the S2A satellite has 13 spectral bands, including 4 bands of a 10 m resolution and 6 bands of a 20 m resolution (Table 3) retained for spectral modeling.

Both images were atmospherically corrected using the ATCOR2/3 radiative transfer model [START_REF] Richter | ATCOR2/3 User guide[END_REF].

Both study sites are characterized by urban activity and by the presence of bare soil, water and vegetation. To mask no-soil pixels from the S2 data, the following approach was applied. First, urban and water areas were masked using Land Parcel Registry maps. In turn, only cropland and vineyards were maintained across the study areas. Second, pixels with normalized difference vegetation index (NDVI) values exceeding an expert-calibrated threshold were masked. A value of 0.35 was determined for both sites after considering field observations (section 2.2.3). The NDVI was retrieved using bands of 842 nm and 665 nm.

Finally, bare soils of the Versailles Plain represent 8.05% of our study area and 44,979 S2A pixels, while bare soils of the Peyne catchment represent 38.6% of our study area and 46,971 S2A pixels.

Models for soil property prediction

The partial least squares regression (PLSR) method was used to construct prediction models based on bare soil samples drawn from each study region. The PLSR method relates two data matrices, the matrix of explanatory variables X (here the soil spectra) and the matrix of dependent variables Y (here the soil properties), through a linear multivariate model, but it extends beyond traditional regression methods in that it also models the structures of X and Y [START_REF] Geladi | Partial least squares regression: a tutorial[END_REF][START_REF] Wold | PLS-regression: a basic tool of chemometrics[END_REF]. For Y in particular, PLSR extracts orthogonal or latent predictor variables accounting for variation in the dependent variable.

The PLSR approach enables one to analyze data based on several noisy collinear features [START_REF] Wold | PLS-regression: a basic tool of chemometrics[END_REF], and it has been used widely for hyperspectral remote sensing (e.g., Selige et al., 2008;[START_REF] Stevens | Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy[END_REF]Gomez et al., 2012;[START_REF] Vaudour | Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra[END_REF].

Prior to PLSR modeling, in order to maximize the fitting of target values against spectra, a series of transforms were tested for either spectra or target values. Reflectance values of the whole spectra were centered. For the Versailles Plain, Clay, SOC and CaCO3 content levels and CEC were log-transformed, while for both areas, the square root of iron content was used.

For La Peyne, log-transform of target values was not retained, as it did not bring any improvement of the fitting. Outliers were detected for sets of image spectra or for sets of measured soil properties. This was based on the 5% threshold of the standardized Mahalanobis distance (Mark and Tunnel, 1985) and on the multivariate outlier detection approach elaborated by [START_REF] Filzmoser | Multivariate outlier detection in exploration geochemistry[END_REF]. Outliers selected through both approaches were retained as outliers.

For each target soil property Y, a PLSR model was constructed from preprocessed image spectra with 10 selected spectral bands (Table 3). The optimal number of latent variables was determined from the prediction residual error sum of squares (PRESS). Due to the limited number of bare soil samples available, and because this study was more focused on comparing performance levels between regions than on carrying out external validation assessments, a leave-one-out cross-validation procedure was applied (Wold, 1978;Gomez et al., 2012). The quality of model fit was evaluated from the root mean squared error of crossvalidation (RMSECV), from the coefficient of determination of cross-validation (Rcv²) and from the residual prediction deviation (RPD), i.e., the ratio between the standard deviation of the calibration dataset to the RMSECV. According to [START_REF] Chang | Near infrared reflectance spectroscopy: principal components regression analysis of soil properties[END_REF] and Viscarra-Rossel et al. (2006), values of 1.4 to 1.8 denote models exhibiting moderate levels of predictive capacity; values of between 1 and 1.4 denote models exhibiting poor levels of predictive capacity, and values of <1 denote very poor models that should not be used.

PLSR models were used through R version 3.2.1 (R Development Core Team, 2015) employing the "pls" package [START_REF] Mevik | The pls package: principal component and partial least squares regression in R[END_REF].

Variograms of predicted soil properties

For the best predicted soil properties, PLSR models were applied to pixels of bare agricultural soil. The spatial structure of the predicted pixels was examined and compared with that of measured properties for the same locations. Spatial structures can be described from a variogram that relates the spatial dependence or semi-variance between points at a distance of h, i.e., the average variance between any pair of sampling points [START_REF] Goovaerts | Geostatistics for natural resources evaluation[END_REF]:

ℎ = ∑ - + ℎ ² (1)
where γ(h) is the average semi-variance of the soil property, n is the number of pairs of sampling points, p is the value of the property P, x is the coordinate of the point, and h is the distance between pairs or the lag value.

For each best predicted soil property, two empirical variograms were built from equation (1): the first was built from 143 or 104 values of the soil samples measured by physico-chemical analysis; the second was built from predicted values for the same locations.

Simple omnidirectional variograms were calculated, and following Gomez et al. (2012), the spherical or exponential model used was fitted through a weighted least squares procedure [START_REF] Pebesma | Multivariable geostatistics in S: the gstat package[END_REF] using the R statistical software program and the Gstats package. The universality condition (no drift) was assumed.

For the Versailles Plain, it was not possible to obtain experimental variograms suitable for fitting a model due to the limited number of sites involved and the clustered sampling scheme employed. Variograms were therefore only examined for the Peyne Valley. To examine the robustness of spatial structures at the scale of the entire study region, variograms

were calculated from all bare soil pixels.

Results

Description of soils samples

Basic statistics on all of the soil properties measured are shown in Table 2. For the Peyne Valley, the range of clay contents present is higher than that of the Versailles Plain, with the highest value recorded as 452 g.kg -1 , though the 1 st quartile and median values (185.0 and 231.0 g.kg -1 ) are slightly lower than those for the Versailles Plain (192.5 and 249.5 g.kg -1 ), while the 3 rd quartile is slightly higher (290.5 g.kg -1 ) than that for the Versailles Plain (281 g.kg -1 ). SOC, silt and CEC levels are measured within a narrower and lower range for the Peyne Valley and with a much lower median (SOC, 8.92 g.kg -1 ; silt content, 322 g.kg -1 ; CEC, 10.65 cmol+.kg -1 ) than that for the Versailles Plain (SOC, 15.9 g.kg -1 ; silt content, 542 g.kg -1 ; CEC, 17.10 cmol+.kg -1 ). Conversely, sand and calcium carbonate content levels are higher in the Peyne Valley, with a higher median (sand content, 433 g.kg -1 ; calcium carbonate content, 136 g.kg -1 ) than that for the Versailles Plain (sand content, 157.5 g.kg 1 ; calcium carbonate content, 27 g.kg -1 ). Consequently, pH values are higher and less variable for the Peyne Valley, while soils of the Versailles Plain vary more in terms of pH levels. Red Mediterranean soils resulting from geochemical weathering contain more free iron, and hence their presence has induced a higher range of Fe content levels, with the 3 rd quartile reaching a value of 1.58 g/100 g compared to a value of 0.97 g/100 g found for the Versailles Plain.

Moreover, coarse fragment content levels in topsoil can reach percentages of as high as 20% and 30% for the 3 rd quartile of the Versailles and Peyne sets, respectively. For the Versailles Plain, the highest correlations (|R| > 0.7) were observed between CEC and SOC content levels, between CEC and clay levels, between CEC and pH levels and between CaCO3 and silt levels (Table 4). Modest correlations (0.7 > |R| > 0.5) were observed between pH and SOC levels, between pH and CaCO3 levels, between pH and sand levels, between Iron and CaCO3 levels and between SOC and clay levels (Table 4). For the Peyne catchment, the highest correlations (|R| > 0.7) were observed between CEC and clay levels and between sand and silt levels (Table 5). Modest correlations (0.7 > |R| > 0.5) were observed between pH and CaCO3 levels, between iron and CaCO3 levels, between sand and clay levels and between silt and CaCO3 levels (Table 5).

Performance of the prediction models

A PLSR model was built from S2A spectra and from each observed soil property associated with the available soil samples. No spectral outliers were removed from the calibration database, regardless of the study area concerned, and the number of latent variables was determined following the rule of the first local minimum of the RMSECV, which varied between 2 and 9 (Table 6). The predictive capacities of the PLSR models ranged from low to medium to high, with RPD values of between 1.0 and 2.0 and with R²cv of between 0.02 and 0.75 (Table 6). Three groups of variables can be identified from the cross-validation performance of the PLSR models (

Soil property mapping from SENTINEL2 data

While retaining PLSR models for groups i and ii, equations were applied to bare cropland and vineyard pixels (Fig. 4).

To relate the output maps to soil data and available soil maps and to facilitate the visual analysis of results for the other maps, and particularly for the Versailles Plain, for which the percentage of mapped pixels is very low, values were simplified to decile classes of predicted bare soil pixels for each study zone (Fig. 4).

The spatial patterns of clay correspond with known soil patterns for both regions. In the Peyne Valley, spatial patterns clearly show chromic luvisols developed along Plio-Quaternary alluvial terraces and characterized by high clay, iron and CEC levels. This result contrasts well with areas with Miocene marine deposits of calcisols and calcaric leptosols with less clay, iron and CEC. However, very high clay and iron content levels predicted for the minor riverbed of the Peyne River (the violet band in the clay map shown in Fig. 4) are not in accordance with our soil knowledge, likely revealing a perturbation related to soil moisture levels.

In the Versailles Plain, the highest predicted clay content levels match our observations of stagnic colluvic cambisols in lower slopes and valleys, whereas the lowest predicted clay content levels as expected were found along the plateaus with luvisols of loess origin. The highest SOC content levels were predicted both for stagnic colluvic cambisols in the valleys and for calcaric cambisols across the slopes, while low SOC content levels were predicted for luvisols originating from loess sources, in accordance with previous studies of the same region [START_REF] Vaudour | Potential of SPOT multispectral satellite images for mapping topsoil organic carbon content over peri-urban croplands[END_REF][START_REF] Vaudour | Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra[END_REF]Zaouche et al, 2017).

Spatial structures retrieved from the set of predicted pixels

Empirical variograms were computed from the 46,971 pixels of bare soil in the Peyne catchment (Fig. 6). They exhibit different spatial structures from those revealed by variograms built from the measured sites only (Fig. 5). From an increase in the number of sites tested, short-scale variations are found across the clay and iron variograms as a result of a first slope break at 250 m that is not shown in the previous variograms (Fig. 6). Another slope break at approximately 1,000 m in the variograms shown in Fig. 6 is still visible but is less pronounced. This more complex spatial structure reveals a combination of soil variations acting at different scales in accordance with our understanding. The broad-scale variability found could be related to the spatial distribution of parent materials of soils with dissimilar clay and iron content levels. Short-range variations may result from erosion-redeposition processes occurring along slopes that involve neighboring parent materials with dissimilar iron and clay content levels.

Discussion

Performance of the S2A-derived PLSR models

Comparisons of the performance of the S2A-derived models for two contrasting agroecosystems suggest that the predictability of a given property does not solely rely on the property itself or particularly on its spectral behavior as a "chromophore" according to Ben [START_REF] Ben-Dor | Using imaging spectroscopy to study soil properties[END_REF]. Rather, if such conditions could explain predictive capacities, both iron and SOC content levels would be predicted for any study area, as iron oxides and organic matter are spectrally influent due to their chemical compositions and arrangements, while their specific influent absorption bands are covered by MSI bands. In particular, SOC is known to be spectrally sensitive across the entire VNIR-SWIR spectral range [START_REF] Ben-Dor | Using imaging spectroscopy to study soil properties[END_REF]Demattê et al., 2015), while the specific absorption wavelengths of iron oxides (550 nm and roughly 860-900 nm) are overlain by green band b3 and near-infrared band b8A. As was expected for SOC, loading values for SOC prediction models were found to be higher overall for the visible b4, b5, near infrared b8A and two SWIR bands (b11, b12) for the Versailles Plain than for the Peyne valley. For iron, loading values were found to be marked for the b3 and b8A bands for the Peyne Valley while they were weakly expressed for the Versailles Plain (Fig. 7). This does not apply for both areas, suggesting that more conditions must be applied to confer predictive capacity. Moreover, clay predictions may be linked to the physical structure of soils in conjunction with a chromophore of approximately 2,200 nm that is fully covered by the b12 band (Table 1): this was revealed by higher loading values (Fig. 7)

. However, performance of clay predictionwas only found to be near-intermediate (0.5 > R²cv > 0.4) for both study areas, suggesting that other conditions may hamper predictive capacity levels. 6). Specifically, calcareous soils of the Versailles Plain are as poorly predicted as those of the Peyne Valley. In addition, CaCO3 degrades model accuracy levels for SOC content, as the lab uncertainty of SOC measurements increases with CaCO3 content levels [START_REF] Vaudour | Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra[END_REF].

Third, the number and composition of sample sets is likely to influence prediction performance. For the Versailles Plain, strong performance recorded for CEC must be considered with caution, as it is based on a small sample size (39 samples) and the model may be overfit.

Fourth, soil surface conditions can be disturbed by soil roughness, soil moisture, emerging and non-covering vegetation, dry vegetation, vine stock and coarse fragment areal coverage. In addition to commonly known difficulties associated with PLSR models in terms of predicting high soil property values (e.g., for SOC [START_REF] Stevens | Soil organic carbon predictions by airborne imaging spectroscopy: comparing cross-validation and validation[END_REF]), which are inherent to linear models, some additional disturbances can arise from these specific soil surface conditions. As a matter of fact, in the Peyne Valley, higher iron content levels are found among very stony soils with coarse fragment content levels of greater than 40%: the higher the coarse fragment content level, the more significant iron content prediction errors become (Fig. 8). In the Versailles Plain, because calcareous soils are stony and as their rock fragments are made of limestone, such relationships merge with those of calcium carbonate content: the higher the coarse fragment content level, the higher the CaCO3 content level and the higher the SOC content level. The highest levels of CaCO3 content (> 136 g. kg -1 ) show the highest levels of SOC residue prediction (Fig. 9). Of course, it cannot be inferred from Figs. 8 and 9 that coarse fragment content alone causes prediction errors, but presumably it contributes to such errors.

In terms of vegetation features, the composition and adequacy of the dataset is conditioned by the NDVI threshold. Regarding dry vegetation, while the b12 band covers the 2,100 nm specific absorption wavelength for cellulose [START_REF] Daughtry | Discriminating crop residues from soil by shortwave infrared reflectance[END_REF], the MSI bands do not enable one to derive an index of dry vegetation such as the Cellulose Absorption Index (CAI = 0.5 (R2000 nm+R2200 nm)-R2100 nm) elaborated by [START_REF] Nagler | Plant litter and soil reflectance[END_REF]. The CAI relies on two wavelengths covered by the b12 band (2,100 and 2,200 nm), but it requires the use of the 2,000 nm wavelength not covered by any MSI band. However, other indexes, such as the Normalized Difference Tillage Index defined for Landsat Thematic Mapper (TM) bands (NDTI= (TM5-TM7)/(TM5+TM7)) [START_REF] Van Deventer | Using Thematic Mapper data to identify contrasting soil plains and tillage practices[END_REF], could be adapted and tested for discriminating crop residue cover. The S2A data thus show limits in accounting for crop residue cover and vine wood vegetation more than the MSI spatial resolution does not enable one to discriminate between vine rows and vine interrows to remove the spectral influence of vine woods.

In

Maximizing the mapped area

As the presence of vegetation severely hampers the prediction of soil properties from VNIR/SWIR remote sensing data, it is important to retrieve as many bare soil signals as

possible. When only one image is considered, as was done in this study, this involves a careful selection of acquisition periods. Images of the Versailles Plain and Peyne Valley were acquired for the spring period to maximize the size of the area with bare soils. In temperate agroecosystems like that of the Versailles Plain, this corresponds to the sowing of spring cereals, with the winter period being less suitable due to impacts of the Nitrate Directive that requires intermediate crop cultivation [START_REF] Vaudour | Potential of SPOT multispectral satellite images for mapping topsoil organic carbon content over peri-urban croplands[END_REF]. In a Mediterranean agroecosystem like that of the Peyne Valley, the spring period corresponds to maximum tilled vineyard coverage for removing weeds [START_REF] Paré | Etude de la variabilité et de la dynamique des états de surface des sols viticoles méditerranéens[END_REF] et al., 2015). Zaouche et al (2018) found improvement incorporating the green band of SPOT into a joint Bayesian model, and development of their approach is in progress for Sentinel2.

Conclusion

S2A image spectra acquired from two French study areas representative of temperate and Mediterranean agroecosystems-the Versailles Plain and the Peyne Valley-were tested as input data of a chemometric model (PLSR) to predict 8 topsoil properties of bare soil areas:

8% and 39% of the Versailles Plain and the Peyne Valley, respectively. Six of these 8 soil properties (clay, SOC, iron, CaCO3, pH, and CEC levels) can be predicted with varying success depending on their intrinsic spectral properties, intra-regional variances, correlations and soil surface conditions. Some of the best predictions were also found to serve as good approximations of the spatial patterns of soil properties.

Although a significant decline in the prediction performance of those obtained from hyperspectral data for the same study areas was observed, S2A soil predictions can serve as valuable inputs for Digital Soil Mapping. With the current absence of efficient hyperspectral satellites that can deliver hyperspectral data, it is advantageous to allow for the mosaicking of multidate acquisition and for the selection of the best acquisitions over a time series. We therefore recommend their use to improve the performance of Digital Soil mapping predictions.

also indebted to Yves Blanca, IRD-UMR LISAH Montpellier, for the soil samples sampling in 2009 over the vineyard Plain of Languedoc.

Zaouche, M., Bel, L., Vaudour, E., 2017. Geostatistical mapping of topsoil organic carbon and uncertainty assessment in Western Paris croplands (France). Geoderma Regional

Figure 1 .

 1 Figure 1. Locations of soil samples from the Versailles Plain (left, infrared colored S2A

  Fig. 2. The brightest soils comprise: (i) calcic or calcaric sandy soils originating from calcareous sandstone (La Peyne); (ii) silt-loam luvisols (Versailles) or eluviated horizons of the chromic luvisols (La Peyne), developing a slaking crust ; (iii) rendzic or calcaric cambisols with very high topsoil calcium carbonate content or outcropping underlying chalk (Versailles). The darkest soils are clayey and alluvial and, for Versailles, can have high organic carbon content (>20 g kg-1) and/or high roughness dating from late winter ploughing. Intermediate soil spectra comprise soils with either moderate rock fragment content (10-25%), or varied textures and intermediate to low roughness (both regions), and also red Mediterranean soils (La Peyne).

Figure 2 .

 2 Figure 2. Selection of reflectance image spectra covering the whole dataset reflectance range

Figure 5 .

 5 Figure 5. Empirical variograms calculated for clay, iron and CEC for the Peyne Valley from

Figure 6 .

 6 Figure 6. Empirical variograms computed for clay, iron and CEC from predicted soil

Figure 7 .

 7 Figure 7. Loading plots for SOC, Fe and Clay of both study areas

Figure 8 .

 8 Figure 8. Relationships between quartiles of coarse fragment content and Fe content (left) and

Figure 9 .

 9 Figure 9. Relationships between quartiles of coarse fragment content and SOC content (left)

Figure 1 .Figure 2 .Figure 5 .

 125 Figure 1. Locations of soil samples from the Versailles Plain (left, infrared colored S2A

Figure 6 .

 6 Figure 6. Empirical variograms computed for clay, iron and CEC from predicted soil

Figure 7 .

 7 Figure 7. Loading plots for SOC, Fe and Clay of both study areas

Figure 9 .

 9 Figure 9. Relationships between quartiles of coarse fragment content and SOC content (left)

Table 1 .

 1 Soil datasets used for models and statistics on soil properties

	Soil	Description	Unit				Versailles Plain					Peyne Valley
	property
				Sample	Min	Q1 Median	Q3	Max	Sample	Min	Q1	Median Q3	Max
				size						size
	SOC	soil organic C g.kg -1	72	7.0	12.5	15.9	20.1 31.9	104	4.0	7.0	8.9	11.4	21.8

Table 2 .

 2 Main characteristics of the studied scenes

	Imaging date	Sensor	Output resolution (m)	Time of acquisition (U.T GMT)	Viewing incidence angle (°)	Sun azimuth (°)	Sun elevation (°)
	12 March 2016	S2A	20	10:50:37	<5.1	160.5	36.1
	19 March 2016	S2A	20	10:40:32	<3.3	157.6	43.7

Table 3 .

 3 Characteristics of the Multi-Spectral Instrument aboard the SENTINEL2 satellite.

	Spectral S2A bands used are shown in bold.
	Spectral band	Spatial resolution (m)	Central wavelength (nm)	Band width (nm)
	b1	60	443	20
	b2	10	490	65
	b3	10	560	35
	b4	10	665	30
	b5	20	705	15
	b6	20	740	15
	b7	20	783	20
	b8	10	842	115
	b8A	20	865	20
	b9	60	945	20

Table 4 .

 4 Pearson correlation table of common soil properties for the Versailles Plain

	Variables	Clay	Silt	Sand	CaCO3	SOC	Fe	pH	CEC
	Clay	1.00
	Silt	-0.24	1.00
	Sand	-0.34	-0.27	1.00
	CaCO3	-0.02	-0.70	-0.33	1.00
	SOC	0.53	-0.38	-0.44	0.46	1.00
	Fe	0.48	0.24	0.03	-0.52	0.00	1.00
	pH	0.46	-0.43	-0.50	0.60	0.66	-0.25	1.00
	CEC	0.77	-0.32	-0.52	0.33	0.82	0.12	0.77	1.00
	Values in bold are different from 0 at a significance level of alpha=0.05

Table 5 .

 5 Pearson correlation table of common soil properties of the Peyne Valley

	Variables	Clay	Silt	Sand	CaCO3	SOC	Fe	pH	CEC
	Clay	1.00
	Silt	-0.14	1.00
	Sand	-0.54	-0.76	1.00
	CaCO3	-0.08	0.53	-0.40	1.00
	SOC	-0.19	0.08	0.06	0.11	1.00
	Fe	0.42	-0.26	-0.06	-0.50	-0.11	1.00
	pH	0.02	0.42	-0.37	0.52	0.03	-0.42	1.00
	CEC	0.89	-0.16	-0.45	0.00	-0.02	0.16	0.14	1.00
	Values in bold are different from 0 at a significance level of alpha=0.05

Table 6

 6 In overall, the Versailles Plain includes all 4 properties of group i (SOC, pH, CEC and CaCO3) and one property of group ii (Clay), whereas the Peyne region includes 3 properties of group ii (Clay, Iron and CEC), with the remaining properties (silt and sand content for both regions and SOC, pH, and CaCO3 content for the Peyne site) are categorized as group iii.

	, Fig. 3): i) soil property models yielding R²cv ≥ 0.5

RPD values of roughly 1 or models exhibiting poor or very poor levels of predictive capacity.

Table 6 .

 6 Cross-validation performance statistics derived from the PLSR algorithm for soil

	Soil property		Versailles Plain			Peyne Valley
		R²cv	RMSEcv RPD	NLV	R²cv	RMSEcv RPD	NLV
	SOC	0.56	1.23	1.51	4	0.02	3.71	1.00	4
	CaCO3	0.48	20.3	1.39	4	0.15	122.2	1.08	8
	Clay	0.39	1.23	1.30	6	0.42	56.4	1.31	5
	Silt	0.14	103.0	1.09	3	0.11	91.9	1.06	9
	Sand	0.22	81.7	1.14	4	0.03	113.0	1.02	7
	Iron content	0.05	0.09	1.02	2	0.45	0.04	1.34	6
	pH	0.51	0.51	1.43	4	0.08	0.64	1.03	4
	CEC	0.75	1.23	2.00	6	0.41	2.41	1.31	6

property prediction (NLv, number of latent variables; RMSEcv expressed in original units).

R²cv and RPD rounded values of greater than or equal to 0.5 and 1.4, respectively, are shown in bold (group i). RPD rounded values of greater than or equal to 1.3 are shown in italics (group ii). 18 Figure 3. Predicted vs. observed soil properties for the Versailles Plain (left) and Peyne Valley (right) for 6 soil properties best predicted for at least one region A model property that exhibits intermediate or near intermediate predictive capacity for one region can exhibit poor capacities for another. This is the case for pH, SOC, iron and CaCO3 content levels. A model property that exhibits high predictive capacity for one region can exhibit near intermediate capacities for another: this is the case for CEC levels.

  conclusion, our use of two contrasting agroecosystems datasets denotes what is achievable from S2A data in terms of predictive capacities for a single considered date. The soils considered in this study are located in French regions but widely develop abroad, across the temperate and Mediterranean areas. Assessment of Sentinel-2 performance is developing for other French regions with other soil types and further for tropical soils.notably coarse fragment content and vegetation. This distinguishes our models from those constructed from simulated MSI spectra derived from lab spectral libraries, as anticipated by[START_REF] Castaldi | Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon[END_REF] for a number of forthcoming or newly launched satellites, including Sentinel-2. However, with the addition of noise and atmospheric effects and from the use of untransformed lab reflectance spectra sampled from central and southern Italy, these authors obtained validation results similar to our performance results for clay) that are inferior to those found for the Versailles Plain in terms of SOC content levels (R² 0.36; RPD 1.26) in the best case or that are not as predictive as found for the Peyne (R² 0.13; RPD 1.09) in the worst case.[START_REF] Gomez | Sensitivity of clay content prediction to spectral configuration of VNIR/SWIR imaging data, from multispectral to hyperspectral scenarios[END_REF] used VNIR/SWIR hyperspectral airborne data (initial spectral resolution of approximately 5 nm) to simulate, among others, Sentinel-2, Landsat 8 and Landsat 7 spectral resolution data. They showed that these multispectral sensors provide very modest performances of clay content estimations, as these spectral resolutions do not provide clay absorption feature useful in regression models.

  10, 126-137.Were, K.,Bui, D.T., Dick, Ø.B., Singh, B.R., 2015. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators 52, 394-403. https://doi.org/10.1016/j.ecolind.2014.12.028 Zaouche, M., Bel, L., Tressou, J.,Vaudour, E., 2018. Soil organic carbon modelling using

	jointly different	sources.	METMA2018,	Montpellier	(France),	poster.

http://metma2018.sfds.asso.fr/assets/Download/proceedingsMETMA9.pdf

List of figure captions

Acknowledgment

This study was carried out in the framework of the TOSCA "Cartographie Numérique des Sols (CNS)" program of the French Space Agency (CNES). It also benefited support from the TOSCA-PLEIADES-CO program of the CNES for the Versailles Plain. Special thanks to Dalila Hadjar, Arthur Scriban, Paulina Michura, for technical assistance in the field. We are

through tests of soil predictions from hyperspectral data (Gomez et al, 2012). The predicted iron variogram has the same nugget and range as the measured one, with only a difference in 'sill' values (the slope of the variogram). No clear explanation can be given for this difference in behavior.

Comparisons with other sensors

Our model performs in a similar manner as those employed in other studies. It performs better than SPOT multispectral satellite images in measuring SOC, and it performs similarly or less optimally than hyperspectral airborne images in measuring all properties.

For the Versailles Plain in particular, previous models of SOC content were derived from multispectral or hyperspectral fields or from image spectra. In terms of RMSECV and RPD values in particular, the regional PLSR model obtained SOC content levels from S2A spectra (1.23 g.kg -1 and 1.51, respectively) more accurately than real SPOT or simulated SPOT spectra [START_REF] Vaudour | Potential of SPOT multispectral satellite images for mapping topsoil organic carbon content over peri-urban croplands[END_REF] error range 4.5-6.0 g.kg -1 , RPD 0.9-1.3) for the same region. This is presumably attributable to the beneficial role of its SWIR bands and to differences in soil surface conditions, which are similar to those obtained from a VNIR hyperspectral AISA airborne image (no SWIR) for the same region [START_REF] Vaudour | Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra[END_REF] error range 2.82-3.79 g.kg -1 , RPD 1.3-1.6).

For the Peyne Valley, performance outcomes obtained from Sentinel-2 are lower than those obtained from a VNIR-SWIR Hymap airborne image (Gomez et al., 2012), regardless of soil properties involved with dramatic changes in performance found for CaCO3 content effectively predicted from the hyperspectral spectra but not predicted from S2A spectra (see §5.1). However, due to intra-regional variances (SOC) and an absence of correlations with more effectively predicted properties (silt), neither SOC nor silt content levels were predicted from hyperspectral and S2A images. Performance degradation from airborne to spaceborne models has also been found by [START_REF] Steinberg | Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP impaging spectroscopy data: prediction accuracy and influence of spatial resolution[END_REF] for SOC, clay and iron content levels, though these authors used simulated EnMAP satellite spectra.

It must be noted that our models were constructed from actual spaceborne images that account for the effects of atmospheric, signal/noise ratio, and soil surface conditions and Despite this careful selection of periods, coverage levels found for bare soil areas remain modest, especially for the Versailles Plain (Fig. 4).

Bare soil areas could be maximized (at least for temperate agroecosystems) by aggregating multiple acquisition dates. This would allow one to accumulate the spectra of soil areas that change every year and along the crop cycle. Sentinel-2 is particularly well suited to this task, as it provides an image every 5 days, thus increasing the likelihood of acquiring images under clear conditions and across the crop rotation cycle.

Such an aggregation of images for maximizing bare soil areas was already attempted by [START_REF] Diek | Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping[END_REF]. These authors doubled the bare soil area by combining hyperspectral APEX acquisitions over 3 successive years and performed intercalibration from linear regressions of atmospherically corrected spectra for different years. This raises issues relating to accurate atmospheric corrections across dates and to the number and anteriority values of dates. This issue is being studied in reference to the Versailles Plain to consider the directional effects of soil roughness in line with tillage operations (Vaudour et al., 2014a).

Indeed, soil roughness levels can be spatially and temporally retrieved from optical/radar pairs such as S2/S1 (Vaudour et al., 2014b), which could be accounted for during soil properties prediction.

Bare soil coverage levels can also be increased by applying unmixing techniques that isolate bare soil signals from semi-vegetated pixels [START_REF] Bartholomeus | Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy[END_REF][START_REF] Ouerghemmi | Semi-blind source separation for the estimation of the clay content over semi-vegetated areas using VNIR/SWIR hyperspectral airborne data[END_REF]. For the Peyne Valley, such techniques are used to predict clay content levels from hyperspectral data for semi-vegetated areas (NDVI < 0.55), expanding the studied surface from 4% (bare soil) to 63% of the total area [START_REF] Ouerghemmi | Semi-blind source separation for the estimation of the clay content over semi-vegetated areas using VNIR/SWIR hyperspectral airborne data[END_REF]. A similar approach could be applied for S2A soil predictions.

Incorporating S2A data into the framework of digital soil mapping

The soil predictions obtained from S2A are neither precise nor extended enough to be exploited "as is" by end users. They must rather be considered as a new source of soil data that should be used for Digital Soil Mapping (Mc Bratney et al, 2003, Lagacherie et al, 2007), a larger methodological framework that can produce more exhaustive and precise soil maps.

If we examine the soil property predictions obtained in this study with such a perspective in mind, S2A soil predictions obtained from this study could serve as valuable inputs for digital soil mapping for two reasons.

First, S2A data offer insight on the spatial patterns of certain soil properties that could be quantified from variograms that closely resemble those obtained from real measurements.

These variograms could be used to optimize further sampling efforts, as they allow one to compute error variances (kriging variance) for sampling schemes of different densities and spatial distributions and thus determine the sampling efforts required to meet target levels of precision in spatial predictions [START_REF] Mcbratney | The design of optimal sampling schemes for local estimation and mapping of regionalized variables[END_REF].

Second, S2A products could serve as surrogate data to improve the precision of Digital Soil Mapping models (Lagacherie & Gomez;[START_REF] Hagolle | Séries temporelles, SPOT4 Take Five blog[END_REF]. For example, Walker et al. (2017) found the use of hyperspectral data as soft data for co-kriging or cogriging with external drift models to improve prediction performance. Although less pronounced, improvements were still observable when using hyperspectral-based estimations of moderate quality (R 2 < 0.5) like many of those obtained in this study. It is therefore expected that some S2A soil outputs could make improvements while being more available than hyperspectral data.

Finally, the potential of S2 data as a co-variable in DSM model could further be evaluated.

While previous researches used MODIS or LANDSAT data in DSM models, either as spectral indexes, such as the NDVI (e.g., [START_REF] Mishra | Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale[END_REF], or as spectral bands (e.g., Were


Charlemagne Vodougnon

Daniel Gérard

Pierre-Emile Bruand

Emilie Auditeau

Farid Boumediene

Yoro Zohoun

Dieudonné Gnonlonfoun

Dismand Houinato

Pierre-Marie Preux

Knowledge, Attitudes and Practices of health sciences students regarding epilepsy at the end of their curriculum in Benin

Keywords: Epilepsy, health sciences students, last year of training, KAP, Benin

Objective:

The main objective of this study was to evaluate the knowledge, attitudes and practices (KAP) of health sciences students regarding epilepsy at the end of curriculum in Benin.

Methods

: We conducted a cross-sectional survey among medical, pharmacy, nursing and midwife students in their last-year of health sciences training in University of Abomey-Calavi in Benin. Students completed a self-administered questionnaire, containing items addressing demographics, epilepsy treatment, diagnosis, clinical practice, and social tolerance regarding person with epilepsy. Results: The response rate to our survey was 87.7% (n=164). The sample consisted of 64 medical students, 22 pharmacy students, 43 nursing students and 35 midwife students. They were divided into 95 female subjects and 69 male; sex ratio was 0.7. The mean age of participants was 23.6±1.8 years. The mean scores for epilepsy knowledge, practices and attitudes were respectively 7.6±1.7 out of a maximum score of 11, 3.9±1.8 out of 8 and 9.6±0.8 out of 10. The KAP overall score was 21.1±3.4 out of a maximum score of 29.0. The variables associated with the overall KAP score were training school, gender, having heard

INTRODUCTION

Epilepsy is one of the most common chronic disease of the central nervous system [START_REF]Atlas: epilepsy care in the world[END_REF].

It affects more than 70 million people worldwide; most live in low and middle income countries and do not receive appropriate treatment [START_REF] Ngugi | Estimation of the burden of active and life-time epilepsy: a meta-analytic approach[END_REF]. In Africa as well as other parts of the world, epilepsy continues to be perceived by many as a supernatural, contagious, and shameful disease [START_REF] Rafael | Sociocultural and psychological features of perceived stigma reported by people with epilepsy in Benin[END_REF][START_REF] Lim | Stigma and health-related quality of life in Asian adults with epilepsy[END_REF][START_REF] Rafael | Social and cultural representation of Epilepsy in elderly aged 65 and more, during a community survey in two French Departments (Hautevienne and Creuse)[END_REF][START_REF] Adjei | Beliefs on epilepsy in Northern Ghana[END_REF]. This limits the search for appropriate care, and contributes to a greater treatment gap in developing countries as patients are often hidden and stigmatized [START_REF] Rafael | Sociocultural and psychological features of perceived stigma reported by people with epilepsy in Benin[END_REF][START_REF] Lim | Stigma and health-related quality of life in Asian adults with epilepsy[END_REF][START_REF] Meinardi | ILAE Commission on the Developing World. The treatment gap in epilepsy: the current situation and ways forward[END_REF][START_REF] Atadzhanov | Epilepsy-Associated Stigma in Zambia: What factors predict greater felt stigma in a highly stigmatized population?[END_REF].

Healthcare workers including physicians, nurses, clinical officers, pharmacist… are the main stakeholders in the management of person with epilepsy (PWE) particularly in developing countries [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF][START_REF] Elliott | Patient, caregiver, and health care practitioner knowledge of, beliefs about, and attitudes toward epilepsy[END_REF]. It is important that they have good medical knowledge about the disease as well as be aware of its socio-cultural representations in different communities [START_REF] Sands | Effects of an Educational Campaign to Change Employer Attitudes toward Hiring Epileptics[END_REF]. However, studies on perceptions of epilepsy among healthcare workers have shown a lack of knowledge about the disease. A survey conducted among Zambian health care workers (nurses and clinical officers) showed that 54.8% of them think that epilepsy is due to mental illness and 5.6% to a contagious disease [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF]. In Laos, 60.4% of a total of 48 physicians in a survey believed that epilepsy could be transmitted through saliva [START_REF] Harimanana | Is insufficient knowledge of epilepsy the reason for low levels of healthcare in the Lao PDR?[END_REF]. Healthcare workers are not often socially tolerant of people with epilepsy. In the Zambian survey, 25.2% of them would oppose the marriage of their son with a PWE; 3.6% believes a child with epilepsy should never attend school [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF]. In Saudi Arabia, 67% of the 822 medical and paramedical personnel surveyed said they would oppose the marriage of their child with a PWE [START_REF] Alaqeel | Epilepsy: What do health-care professionals in Riyadh know?[END_REF]. Levels of education and occupation were correlated with the level of knowledge about epilepsy and with a better social tolerance of PWE [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF][START_REF] Alaqeel | Epilepsy: What do health-care professionals in Riyadh know?[END_REF]. Healthcare workers who had completed practical internships, in addition to theoretical curriculum on epilepsy during their training, had a better understanding of the disease [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF].

Healthcare workers recognized that their knowledge about epilepsy was limited [START_REF] Hayes | Assessing the true learning needs of health care professionals in epilepsy care[END_REF][START_REF]Patient, caregiver, and health care practitioner knowledge of, beliefs about, and attitudes toward epilepsy[END_REF]. Barriers that limit appropriate management of PWE, identified by healthcare workers, are the difficulties in acute management and recognition of partial epilepsy, in managing pregnant woman with epilepsy and in the managing the stigma [START_REF] Harimanana | Is insufficient knowledge of epilepsy the reason for low levels of healthcare in the Lao PDR?[END_REF][START_REF] Thompson | A qualitative study of carers' and professionals' views on the management of people with intellectual disability and epilepsy: a neglected population[END_REF][START_REF] Keikelame | General practitioners' perceptions on management of epilepsy in primary care settings in Cape Town, South Africa: an exploratory pilot study[END_REF]. Studies conducted among healthcare workers about epilepsy do not always take into account the time elapsed since the end of training in vocational schools [START_REF] Alaqeel | Epilepsy: What do health-care professionals in Riyadh know?[END_REF][START_REF] Keikelame | General practitioners' perceptions on management of epilepsy in primary care settings in Cape Town, South Africa: an exploratory pilot study[END_REF][START_REF] Blank | Understanding referral patterns to an epilepsy clinic: professional perceptions of factors influencing the referral of older adults[END_REF]. Thus, when the level of knowledge seems insufficient, it may be due to a lack of initial knowledge (failure in acquisition during their curriculum) or to a loss over time of knowledge initially acquired. In Cameroon, 6% of medical students considered epilepsy to be contagious, 10% of preclinical students and 3.3% of clinical students could not tell a cause of epilepsy [START_REF] Njamnshi | Knowledge, attitudes, and practice of Cameroonian medical students and graduating physicians with respect to epilepsy[END_REF]. More than 80% of first-year medical students at Mulago Hospital in Kampala said that they would not allow their children to marry PWE. Most respondents believed that epilepsy was a mental illness, and many believed that PWE cannot have normal intelligence [START_REF] Bigelow | Perceptions of epilepsy among first-year medical students at Mulago Hospital in Kampala, Uganda[END_REF]. These misperceptions must be identified and corrected among medical students during the training.

Epilepsy is a major public health problem in Benin as in more sub-Saharan Africa countries, with nationwide representative prevalence of 08.05 [START_REF] Yemadje | Prevalence of epilepsy in the 15 years and older in Benin: a door-to-door nationwide survey[END_REF]. Like other African countries, Benin faces a shortage of neurologist; leaving responsibility for the care of PWE to general practitioners, nurses and midwives; pharmacists are also involved. Benin has three universities in which are trained health care workers. University of Abomey-Calavi is the oldest and has a medical school, a faculty of pharmacy, dentistry, and a nursing and midwifery school. The University of Parakou in the north has a medical school and a training school for nurses. Finally the University of Porto Novo, the latest has a medical school. It takes 7 years to graduate as a Medical Doctor and 6 years as a Pharmacist. Nurses and midwives would get their professional license after 3 years of study. During their curriculum, medical students benefit from 28 hours of neurology classes divided into 12 hours of semiology (2nd year), 10 hours of pathology (4th year) and 6 hours of therapy in 5th year.

The hourly load devoted to epilepsy is 6 hours (2 hours per year). Apart from the therapeutic courses, pharmacists have the same neurology content as physicians, but are taught in 2nd and 3rd year. Nurses and midwives receive, in the third year, the same 8 hours neurology program, including 2 hours devoted to epilepsy. Each of these student categories complete hospital internships during their curriculum; completing this internship in a neurology department is a choice; some students can finish their training without setting foot in a neurology department.

Prior knowledge, attitudes, beliefs, and practice surveys conducted in Benin have been directed at the general public, students, or teachers [START_REF] Rafael | Sociocultural and psychological features of perceived stigma reported by people with epilepsy in Benin[END_REF][START_REF] Rafael | Social and cultural representation of Epilepsy in elderly aged 65 and more, during a community survey in two French Departments (Hautevienne and Creuse)[END_REF][START_REF] Hounsossou | Knowledge, attitudes, and behavior among key people involved in the employment of people with epilepsy in southern Benin[END_REF][START_REF] Adoukonou | Socio-cultural aspects of epilepsy in a rural community in northern Benin in 2011[END_REF][START_REF] Nubukpo | Comparison of sociocultural attitudes towards epilepsy in Limousin (France), in Togo and in Benin (Africa)][END_REF][START_REF] Nubukpo | Psychosocial issues in people with epilepsy in Togo and Benin (West Africa) II: quality of life measured using the QOLIE-31 scale[END_REF][START_REF] Nubukpo | Psychosocial issues in people with epilepsy in Togo and Benin (West Africa) I. Anxiety and depression measured using Goldberg's scale[END_REF]. There is no study to our knowledge that is focused on the knowledge, attitudes and practices of healthcare professionals, much less healthy school students on epilepsy in Benin.

The main objective of this study was to determine the level of knowledge of health students about epilepsy at the end of their curriculum in Benin.

METHODS

We conducted between May 6th and 16th 2016 a cross-sectional survey among health sciences students in their final year of studies in Benin.

Benin is a sub-Saharan Africa country bounded by Burkina-Faso and Niger in the North, Togo in the West and Nigeria in the East. The country is extends from the Niger River in the North to Atlantic Ocean in the South with an area of 112.622 km 2 . The estimation population in Benin was 11.186.785 inhabitants in 2017 [27].

Study setting

The study was conducted at the Faculté des Sciences de la Santé (FSS) and at the Institut National Médico-sanitaire (INMES), two entities of Abomey-Calavi University in Benin. University of Abomey-calavi was chosen because it is the only one that trains both students in medicine, pharmacy, nursing and midwifery. We had ensured they had finished the neurology program, both theoretical and internships.

After having obtained the approvals of the Dean of Faculté des Sciences de la Santé Dean and the Director of Institut National Médico-sanitaire, all students were invited (medical and pharmacy students on the one hand and nurse and midwife students on the other) to participate at a conference on research methodology and epidemiological studies.

The subject of the study had not been communicated to them in advance to avoid information bias. During the conference, the KAP study was presented to students and their free and informed consent was requested. All students present agreed to participate in the study.

Instrument

After reviewing several knowledge, attitudes, beliefs and practices instruments used by other groups studying epilepsy [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF][START_REF] Harimanana | Is insufficient knowledge of epilepsy the reason for low levels of healthcare in the Lao PDR?[END_REF][START_REF] Alaqeel | Epilepsy: What do health-care professionals in Riyadh know?[END_REF][START_REF] Hounsossou | Knowledge, attitudes, and behavior among key people involved in the employment of people with epilepsy in southern Benin[END_REF][START_REF] Vancini | Knowledge about epilepsy among health professionals: a crosssectional survey in Sao Paulo, Brazil[END_REF][START_REF] Panda | Evaluation of knowledge of and attitudes toward epilepsy among the health science students of Manipal University[END_REF][START_REF] Roth | An evaluation of pharmacist knowledge on treatment with antiepileptic drugs[END_REF], we developed a 41-item questionnaire. The most common questions about KAP were kept and other items related to symptoms, diagnosis and management of epilepsy were added (appendix). The questionnaire was divided into two sections. The first section included sociodemographic information and general questions about epilepsy (Q1-Q14). The second part was related to KAP (Q15-Q41) and assessed the basic knowledge that any health provider should have about epilepsy. This part could be subdivided into three areas (knowledge, attitudes and practices). "Knowledge" questions were related epilepsy diagnosis; the "Practices" chapter was related with epilepsy clinical management and "Attitudes" questions were related to social tolerance regarding person with epilepsy.

There were three different types of questions based on the responses that were possible. The first group was closed questions leaving only the choices of yes or no, true or false and I don't know. The second type was multiple choice questions (several qualitative answers were proposed) and recognition responses. The last one collected opinions of the professionals on the different attitudes towards the stigma on epilepsy. The choice of answers was: Strongly disagree, Disagree, Agree, Totally agree.

To facilitate comparison between groups, we developed three scores; "Knowledge score" (Q12-Q22), "Practice score" (Q23-Q30) and "Attitude score" (Q31-Q40). The sub-questions were not taken into account in calculating the different scores since they provided additional information regarding the questions that preceded them. The various response options for each question were reduced to a dichotomous alternative, "Yes"/"No" for the rating. The answers "False" and "I don't know" was deemed equivalent to "No", "True" to "Yes". A correct answer to a multiple choice question became a "Yes", an incorrect answer, or the choice of both correct and incorrect answers to the same question became a "No". For questions on opinions, "Agree" or "Strongly agree" was deemed equivalent to "Yes"; and "disagree" or "totally disagree" to "No". Correct answers were rated 1 and incorrect answers 0. The score for each domain was the sum of all the individual scores for each question in that specific domain. Thus, the scores ranged from 0 to 11, 0 to 8 and 0 to 10 respectively for the knowledge, practice and attitude of healthcare professionals regarding epilepsy. The overall KAP score, which is the sum of the three domain scores, ranged from 0 to 29. To help with the interpretation of the results, the overall KAP score has been transformed into several categories. They consisted in 7 classes [START_REF] Dorota | Attitudes of employees of service and trading companies towards people with epilepsy and their professional activity in Poland[END_REF] : Excellent [START_REF] Nubukpo | Psychosocial issues in people with epilepsy in Togo and Benin (West Africa) I. Anxiety and depression measured using Goldberg's scale[END_REF](27)[START_REF] Vancini | Knowledge about epilepsy among health professionals: a crosssectional survey in Sao Paulo, Brazil[END_REF][START_REF] Panda | Evaluation of knowledge of and attitudes toward epilepsy among the health science students of Manipal University[END_REF], Very good [START_REF] Adoukonou | Socio-cultural aspects of epilepsy in a rural community in northern Benin in 2011[END_REF][START_REF] Nubukpo | Comparison of sociocultural attitudes towards epilepsy in Limousin (France), in Togo and in Benin (Africa)][END_REF][START_REF] Nubukpo | Psychosocial issues in people with epilepsy in Togo and Benin (West Africa) II: quality of life measured using the QOLIE-31 scale[END_REF], Good [START_REF] Bigelow | Perceptions of epilepsy among first-year medical students at Mulago Hospital in Kampala, Uganda[END_REF][START_REF] Yemadje | Prevalence of epilepsy in the 15 years and older in Benin: a door-to-door nationwide survey[END_REF][START_REF] Hounsossou | Knowledge, attitudes, and behavior among key people involved in the employment of people with epilepsy in southern Benin[END_REF], Fairly good [START_REF] Keikelame | General practitioners' perceptions on management of epilepsy in primary care settings in Cape Town, South Africa: an exploratory pilot study[END_REF][START_REF] Blank | Understanding referral patterns to an epilepsy clinic: professional perceptions of factors influencing the referral of older adults[END_REF][START_REF] Njamnshi | Knowledge, attitudes, and practice of Cameroonian medical students and graduating physicians with respect to epilepsy[END_REF], Fair [START_REF]Patient, caregiver, and health care practitioner knowledge of, beliefs about, and attitudes toward epilepsy[END_REF][START_REF] Thompson | A qualitative study of carers' and professionals' views on the management of people with intellectual disability and epilepsy: a neglected population[END_REF], Low [START_REF] Harimanana | Is insufficient knowledge of epilepsy the reason for low levels of healthcare in the Lao PDR?[END_REF][START_REF] Alaqeel | Epilepsy: What do health-care professionals in Riyadh know?[END_REF][START_REF] Hayes | Assessing the true learning needs of health care professionals in epilepsy care[END_REF], Nil (0-11). The Excellent range was considered as the reference level of knowledge.

Question 41 was about students' own judgment about their epilepsy knowledge. The different options in terms of responses were Good, Acceptable and Limited.

The questionnaire was pre-tested on a pilot group of fourth-year medicine students for accuracy and comprehension. This allowed the wording of some of the questions to be adjusted to be better understood by students. The minimum time to complete the questionnaire was 9 minutes and the maximum 25 minutes.

The questionnaire was administered in the form of a self-administered questionnaire.

Questionnaires were completed anonymously under the supervision of investigators so that students will work together to answer.

Statistical analysis

Data analysis was performed using Epi Info 3.5.4. Descriptive variables were expressed as mean, standard deviation, or percentage. The scores were analyzed as continuous variables. A univariate analysis using Kruskal-Wallis test was used to compare the different scores. A p-value of less than 5% was considered statistically significant.

RESULTS

Demographic characteristics and experience with epilepsy

There was a total of 187 health students at the end of their curriculum at the University of Abomey-Calavi, including 76 medical students, 26 pharmacy students, 46 nursing and 39 midwifery students. One hundred and sixty-four (164) subjects participated in the study, representing an overall participation rate of 87.7%. They consisted of 64 medical students, 22 pharmacy students, 43 nurse students and 35 midwife students. They were divided into 95 female subjects and 69 male; the sex ratio was 0.7. The overall average age was 23.6 ± 1.8 years. The most represented age range was 25-29 years old (69.5%) (Figure 1). Male subject's average age was 24.03 ± 1.9 and 23.24±1.6 for female.

Out of the total number of survey participants, 67.7% had witnessed at least one epileptic seizure, while 14% had a close family member/friend with epilepsy. Table 1 shows demographics characteristics of the survey participants.

Health sciences students knowledge about epilepsy

The average knowledge score was 7.6 and ranged from 3 to 11 (standard deviation 1.7, median 8). The different responses to questions on knowledge are reported in Table 2.

Epilepsy was recognized as a neurological disease by almost all subjects (99.4%). On the other hand, its definition as the occurrence of at least two epileptic seizures, unrelated to a particular situation, was known only by half of the subjects. About 30% believed that epilepsy was always hereditary and 6.7% thought (midwives mainly) it was contagious. All subjects knew that epilepsy was not due to divine punishment, nor to a curse or to witchcraft.

Attitudes of health sciences students regarding person with epilepsy

Health sciences students as a whole were quite supportive of people with epilepsy.

All of them stated that children with epilepsy should be educated in the same way as other children and in the same institutions. However, 3.1% of medical students, 13.0% of pharmacy students, 44.2% of nursing students and 40.0% of midwifery students were unfavorable to physical and sports activities for PWE. About 4.7% of nursing students and 8.6% of midwifery students suggested that parents should forbid their child from playing with PWE. All survey participants were of the opinion that PWE could have a child and that parents should not oppose the marriage of their child with a PWE except for 11.4% of midwifery students who stated that parents should discourage their child from marrying a PWE. In the workplace, 4.7% of nursing students, 3.1% of medical students and 2.9% of midwifery were not in favor of PWE sharing the same workspace as healthy subjects. A small number of nursing and midwifery students were of the opinion that public places should be prohibited to PWE (2.3 and 5.7%, respectively).

The average score on attitudes was 9.6 with a range of 9 to 10. All categories had a score greater than 9.

Practices of health sciences students regarding person with epilepsy

In response to what should be done when witnessing someone having a generalized tonic-clonic seizure, 4.7% of medical students, 4.7% of nursing students and 25.7% of midwifery students recommended to prevent the convulsion by overpowering the individual through force. Introduction of an object into the person's mouth to prevent tongue-biting was proposed by one-third of nursing students (32.6%), one-fifth of medical students (20.3%), half of pharmacy students (59.1) and more than one in three midwifery students (65.7). Health sciences students' practices for epilepsy are presented in Table 3.

The mean score on health sciences students' Practices for Epilepsy was 3.9 ± 1.8. The median was 4 and the range was 0 to 8.

Overall KAP score of health sciences students on epilepsy and associated factors

The mean KAP score of health sciences students surveyed was 21.1 ± 3.4 out of 29.

The median was 21, the lowest score was 10 and the highest 28. None of the subjects surveyed had a total score of 29 out of 29. The mean overall KAP score for each category was 23.3 ± 2.4 for medical school students, 21.2 ± 2.6 for pharmacy students, 20.9 ± 2.4 for nursing school students and 17.2 ± 2.9 for midwifery students. Figure 2 shows the ranking according to the qualitative scale of the average scores of each health sciences student's category.

Overall, 11.6% of subjects rated their overall level of knowledge on epilepsy as good, 46.3% as acceptable, and 42.1% as limited.

The variables associated with the overall KAP score were training school, gender, having heard about epilepsy before health sciences studies (p=0.017) and having a relative with epilepsy (p = 0.001) (table 4). Male students and medical school students had significantly better overall KAP score than female students or the other training schools respectively (p <0.001).

A significant positive correlation was found between the level of knowledge and the level of practices (r = 0.29, P <0.001), and with the level of attitudes (r = 0.09, p < 0.001).

DISCUSSION

This study was conducted to determine the level of epilepsy knowledge of health sciences students at the end of their studies in Benin. The participation rate was 87.7%. The mean of overall KAP score was 21.1 ± 3.4 out of 29. It was considered low because a score ranging from 26 to 29 was established as the reference level of knowledge. Male students, those with a close family member or friend with epilepsy, and medical students had a significantly better level of knowledge about epilepsy than others.

The study was conducted using a questionnaire designed after reviewing in literature several knowledge, attitudes, beliefs and practices instruments used by other groups studying epilepsy. A composite score has been created to allow comparison between groups.

Pre-testing ensured that the different questions were understood by the students. The filling of the questionnaire in the presence of the investigator ensured the individuality of the answers. Moreover, the students had only been informed about the study during the working session, this eliminates potentials information bias. The participation rate of about 90% ensures a good representation of our sample.

Despite these positives, our study has some shortcomings. Indeed, while this is obvious, it will be difficult for us to make a pronouncement about the actual contribution of medical studies to students' knowledge because of the study design. A comparative study with other students at the end of non-medical course, or a quasi-experimental study type before / after would have been more appropriate. Also, for more reliability of our collection tool, it would have been interesting to precede our study of a questionnaire validation survey.

The subjects had witnessed an epileptic seizure in 67.7% of the cases. This value is significantly lower than the 80.7% reported by Njamnshi et al. in medical students in Cameroon [START_REF] Njamnshi | Knowledge, attitudes, and practice of Cameroonian medical students and graduating physicians with respect to epilepsy[END_REF]. This proportion exceeds ours because recruitment was done on a larger population (students of the fourth in the seventh year of medicine). Moreover, the low rate obtained by students in pharmacy and midwife who do not attend many cases of seizures because of their work has influenced our proportion (Table 1). Our value is also significantly lower than the 98.2% of controls in the survey of healthcare workers in Zambia [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF]. The difference could be explained by the fact that Zambians were already practicing healthcare providers and, as a result, had more experience with epilepsy.

The notion that epilepsy is a neurological disease was confirmed by almost all of the subjects surveyed. Health sciences' training has certainly helped to remove the socio-cultural representations which attribute a supernatural origin to the disease, or which links it to uncleanliness [START_REF] Rafael | Sociocultural and psychological features of perceived stigma reported by people with epilepsy in Benin[END_REF]. The majority (89.9%) of Cameroon health sciences students recognized that epilepsy is caused by brain disorder. However some health sciences students like firstyear medical students at Mulago Hospital in Kampala thought that epilepsy is a mental retardation (10%) or a spirit possession (5%) [START_REF] Bigelow | Perceptions of epilepsy among first-year medical students at Mulago Hospital in Kampala, Uganda[END_REF]. This findings show that students at the beginning of medical training have less knowledge about epilepsy than their seniors [START_REF] Fonseca | Knowledge and attitude toward epilepsy among health area students[END_REF][START_REF] Tedrus | Knowledge and attitudes toward epilepsy amongst students in the health area: intervention aimed at enlightenment[END_REF].

In a study conducted in India among students who were not studying health sciences, 59.3% claimed that epilepsy was due to mental retardation [START_REF] Pandian | High school students' knowledge, attitude, and practice with respect to epilepsy in Kerala, southern India[END_REF]. These results also confirm the contribution of health sciences training to the understanding of epilepsy. However, in some cases, there can also be misconceptions about epilepsy among health sciences students. Our study showed that 6.7% of students mainly midwives believed that epilepsy is contagious. In Cameroon, only 0.7% of medical students said it was contagious [START_REF] Njamnshi | Knowledge, attitudes, and practice of Cameroonian medical students and graduating physicians with respect to epilepsy[END_REF]. In Laos, 33.3% of physicians thought it was a communicable disease [START_REF] Harimanana | Is insufficient knowledge of epilepsy the reason for low levels of healthcare in the Lao PDR?[END_REF]. This means that even in the health care workers already operating, gaps about epilepsy knowledge exists. Belief in the contagiousness of epilepsy through direct contact with the PWE, or with their body fluids (saliva, urine, tears in this study) can exacerbate attitudes of fear and rejection. Epilepsy was seen as a hereditary disease by 28.7% of the subjects surveyed. This rate is lower than the 40.3% found in a study of medical students at the University of Manipal in India [START_REF] Panda | Evaluation of knowledge of and attitudes toward epilepsy among the health science students of Manipal University[END_REF]. This difference is understandable since the population of the Indian study consisted of students in their first year at medical school.

The subjects in our study had a fairly good social tolerance towards PWE. None of the medical students stated that they would object to their child marrying a PWE or that they would prevent children with epilepsy from going to the same schools as other children, contrary to the opinion of first-year medical students at Mulago Hospital in Kampala who would not allow their child to marry someone with epilepsy [START_REF] Bigelow | Perceptions of epilepsy among first-year medical students at Mulago Hospital in Kampala, Uganda[END_REF]. Physicians in Laos would oppose to the marriage of their children with a PWE (70.8%) and 24.3% wouldn't allow their children going to the same school as children with epilepsy [START_REF] Harimanana | Is insufficient knowledge of epilepsy the reason for low levels of healthcare in the Lao PDR?[END_REF].

As in the case with the general population, generalized tonic-clonic seizure was easily recognized by healthcare professionals at the end of their studies. In our study, only 37.2% of subjects (all categories combined) were able to identify partial epilepsies. This result is higher than the 20% obtained by already practicing medical staff in the Zambian study [START_REF] Chomba | Zambian health care workers' knowledge, attitudes, beliefs, and practices regarding epilepsy[END_REF].

The practice of health sciences students when faced with an epileptic seizure is not without consequence since some actions to avoid were found in the various responses obtained in this study. For example, preventing seizures by controlling the patient by force or giving medication by mouth are attitudes which should be avoided; fractures, dislocations or choking could occur.

Epilepsy can be treated. Our study showed that 25% of nursing students, 25% of pharmacy students, and 20% of midwifery students thought the opposite. The treatment of epilepsy is not always lifelong. Indeed, stopping antiepileptic drugs (AED) could be considered for a treated PWE after two to five years without seizure. Under these conditions, the AED dosage would be gradually reduced until complete discontinuation of the drug. Some of the nurses and midwives in our study believed that PWE had to follow dietary restrictions. This notion is often found in popular beliefs, especially in sub-Saharan Africa, where the consumption of these foods would increase the foam ("drool") that flows from the mouth of the subject during a seizure [START_REF] Nkwi | The epileptic among the Bamileke of Maham in the Nde Division, West Province of Cameroon[END_REF]. Bamileke traditional healers in Cameroon and Akan traditional healers in Ivory Coast banned all meat consumption during the treatment of epilepsy [START_REF] Nkwi | The epileptic among the Bamileke of Maham in the Nde Division, West Province of Cameroon[END_REF][START_REF] François | Traditional health practitioners and epilepsies in Ivory Coast][END_REF]. They did not necessarily make the link between meat consumption and the occurrence of seizures, but they thought that undercooked meat could be the cause of some diseases, without necessarily thinking about epilepsy.

Overall, our study showed that medical students have a better level of knowledge than the rest of the health sciences students. This result confirms what is found in the literature [START_REF] Vancini | Knowledge about epilepsy among health professionals: a crosssectional survey in Sao Paulo, Brazil[END_REF]. Such a result is hardly surprising since, of the four professional categories, medical students benefit from more hours of epilepsy training and do more internship in different hospital departments during their university curriculum. It could also be that the content of the training for medical students is different from that of pharmacy students, even if it is on the same topic. Since medical students will be prescribers, teachers will tend to emphasize what would promote a good understanding of the disease, its diagnosis and its management. Although this difference is not statistically significant in our study, health sciences students who have done internships in a hospital department, in addition to lectures on epilepsy, have a better overall knowledge of the disease. This result reflects the importance of traineeship in hospital settings as part of the training of health sciences students. None of the subjects surveyed were able to correctly answer all of the questions included in the KAP questionnaire and very few had an excellent level of epilepsy knowledge.

These results reflect a relatively low level of knowledge for health sciences students about epilepsy at the end of their curriculum in Benin. The KAP questionnaire, however, included basic questions about epilepsy for which all students should get all the correct answers.

Most of these students were aware of their level of knowledge about the disease. Others, who described their knowledge as good or acceptable, when in fact it was not, represent a potential danger to patients.

CONCLUSION

It appears from this study that the overall level of knowledge about epilepsy among health sciences students at the end of their curriculum in Benin, whatever the category is relatively low. These future healthcare workers will certainly be confronted, on the ground, with the difficulties of taking care of PWE. Actions to improve knowledge and attitudes of health sciences students could improve future management of patients with epilepsy.

• Sanofi Global Health Programs staff • Physicians in neurology specialization studies at University of Abomey-Calavi. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Figure 1 :Figure 2 :

 12 Figure 1: Distribution of health sciences students by age range

Table 1 : Demographic characteristics and experience with epilepsy of health sciences students

 1

	Medicals	Pharmacy	Nursing	Midwifery
	n=64	n=22	n=43	n=35

Table 2 : Knowledge of health sciences students about epilepsy Medicals n=64 Pharmacy n=22 Nursing n=43 Midwifery n=35 At what age can epilepsy occur?

 2

	Any age %	96.9	72.7	93.0	77.1
	What can cause epilepsy?
	Disorder of the central nervous system %	100.0	100.0	93.0	100
	A psychiatric disorder %	6.3	0.0	20.9	22.9
	All children born to parents with epilepsy will develop
	epilepsy
	True %
		4.7	31.8	11.9	42.9

An individual who has sustained a head injury has a higher risk of developing epilepsy in the future

	True %
		81.3	45.5	69.8	20
	Epilepsy always manifests as seizures
	True %	34.4	81.8	60.5	82.9
	All seizures equal epilepsy
	False %	100.0	86.4	100.0	100.0

Type of seizures/Signs may be found during an epileptic seizure

	Generalized seizure (%)	92.2	100	72.1	62.9
	Partial seizure (%)	65.6	0.0	32.6	14.3
	Absence seizure (%)	56.3	18.2	44.2	11.4
	Hallucinations (%)	10.9	0.0	2.3	11.4
	Grinding of the teeth (%)	64.1	0.0	44.2	25.7
	Rolling of the eyes (%)	70.3	18.2	51.2	48.6
	Generalized loss of body tone (%)	56.3	40.9	41.9	28.6
	Knowledge score (mean, standard deviation)	8.7 (1.3)	7.2 (1.4)	7.6 (1.3)	5.7 (1.5)

Table 3 : Practices of health sciences students regarding person with epilepsy Medicals n=64 Pharmacy n=22 Nursing n=43 Midwifery n=35 What should you do when faced with a generalized tonic-clonic seizure?

 3

	-Stop the seizure by overpowering the
	individual through force (%)	4.7	0.0	4.7	25.7
	-Place an object in the individual's mouth
	to prevent the jaws from closing (%)	20.3	59.1	32.6	65.7
	-Protect the individual by moving them
	away from dangerous objects (%)	90.6	63.6	95.3	68.6
	-Administer anticonvulsants via the mouth
	(%)	18.8	9.1	7.0	0.0
	-Comfort the individual until the seizure is
	over (%)	81.3	40.9	88.4	65.7
	Epilepsy can be treated
	True (%)	100.0	81.8	79.1	71.4

An individual with epilepsy can be permanently cured of the disorder

	True %	23.4	27.3	14.0	11.4
	Pharmaceutical antiepileptic treatment continues
	throughout the individual's life
	True %	45.3	45.5	74.4	37.1

The individual can stop taking the antiepileptic drugs as soon as the seizures stop

	True %	15.6	22.7	18.6	0.0

Individuals with epilepsy must adhere to a certain number of dietary restrictions

	True %	0.0	0.0	16.3	37.1

Which of the following situations are not advisable for individuals with uncontrolled epilepsy?

	Teaching	14.1	9.1	46.5	25.7
	Working at height	95.3	100.0	86.0	68.6
	Driving	89.1	100.0	88.4	85.7
	Practice Score (mean, standard deviation)	4.7 (1.6)	4.1 (1.6)	3.9 (1.4)	2.3 (1.4)

Table 4 : Variables associated with the overall level of knowledge about epilepsy

 4

	Number (%)	Overall KAP	p value
		score

ACKNOWLEDGEMENTS

• Healthcare professionals in the last years of medical and paramedical studies at University of Abomey-Calavi

CONFLICT OF INTEREST STATEMENT