Identifying and mapping terrons in Denmark

Geoderma 363 (2020) 114174

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Identifying and mapping terrons in Denmark a,⁎

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T b

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Yi Peng , Yannik E. Roell , Anders Bjørn Møller , Kabindra Adhikari , Amélie Beucher , Mette B. Grevea, Mogens H. Grevea a b

Department of Agroecology, Faculty of Science and Technology, Aarhus University, Blichers Allé 20, PO Box 50, DK-8830 Tjele, Denmark University of Arkansas, Department of Crop, Soil, and Environmental Sciences, Fayetteville, AR 72701, USA

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Alex McBratney

Spatial assessment of terroir is creating a new possibility for enhancement of high quality agro-food product and to minimize negative environmental effects such as soil degradation and associated risks. The classification and mapping of particular terroir units could be a competitive marketing tool with a major impact on farmers’ incomes. For this purpose, Carré and McBratney (2005) proposed the terron concept to establish combined soil and landscape entities as the first investigative step to identify terroirs. The main objective of the present work was to assemble various environmental factors (i.e. soil, terrain and climate), to identify and then to map terrons in Denmark. First, for representing soil factors, a national soil spectral library was utilized to measure taxonomic distances between 34 Danish reference soil profiles and the Danish national soil profile database (586 soil profiles). Second, the terrain and climate factors for each soil profile location were then compiled as represented by relative slope position, valley depth, valley bottom flatness, vertical distance to the channel network, number of frost days, annual number of growing days, global solar radiation, and precipitation. Third, nine Danish terron classes were established by fuzzy c-means clustering based on an integrated matrix including all soil, terrain and climate factors whereby each terron class is characterized by soil, terrain and climate as a whole entity. Finally, the spatial distribution of Danish terrons was mapped using Cubist regression rules. The results were compared with a soil map derived from the same profile database. We concluded that the map of terrons described natural environment quantitatively and formally in terms of soil, landscape and climatic information better than just a soil class or soil attribute map. Further investigations are needed to discover whether the terron classes give better predictions of landscape-dynamic processes and allow better management options than soil alone. This study also demonstrated several advantages of using soil spectral data and ancillary data to identify and map terrons. The next step will be to validate the terron map by incorporating crop yield data and social factors to delineate natural Danish terroir units.

Keywords: Terron Vis-NIR Digital soil mapping Soils Terrain Climate

1. Introduction Terroir is a French word (literal translation is lands with specific characteristics), a concept originally developed for the viniculture management and used as a model for wine appellation and regulation. The terroir concept covers both the natural factors (e.g. specific soils, topography, climate, landscape characteristics and biodiversity features) and social factors (e.g. history, culture, traditions, reputation) over space and time (Barham, 2003; Carey et al., 2002; Dougherty, 2012; Vaudour et al., 2015). During the last twenty years, Europe has witnessed an increasing adoption of the terroir concept in relation to agro-food products (Barham, 2003; Kapur and Ersahin, 2014). The terroir effect is the distinctive qualities and taste imparted to both beverages and food products specific to the environment where they

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have originally been produced. Therefore, terroir plays an important role for the differentiation of agricultural areas, which is tightly linked to the field of geographical indications (Bowen and Zapata, 2009). Spatial assessment of terroir is now a new frontier for development of agro-food production quality and to minimize negative environmental effects such as soil degradation and emission from agriculture. Accordingly, authorities, environmental agencies, private food sectors and scientists have been asked to help and identify the most important aspects of terroir and its boundaries for land use planning, enterprise suitability assessments and rural development policies. A good understanding of the spatial variability of environmental factors is very important to manage and preserve soils and face the current and future issue of climate change. Besides, from a business point of view, the advertising and labelling of food products on the market based on

Corresponding author. E-mail address: [email protected] (Y. Peng).

https://doi.org/10.1016/j.geoderma.2020.114174 Received 21 October 2019; Received in revised form 19 December 2019; Accepted 6 January 2020 0016-7061/ © 2020 Elsevier B.V. All rights reserved.

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main consideration for this work was to most effectively assess and use the soil information available within a given database. In order to enhance the value of available soil data, Vis-NIR data were used to estimate and map clay mineral types and iron oxides. Therefore, 12 terron classes were established in their study based on eight landform variables and eight soil variables. The use of soil and spectral inference functions to enhance the value of field-collected data was one of the novelties in their work. The authors also foresaw that climatic variables such as temperature, growing degree-days and incidence of frost risk would improve and refine terrons to capture the complexity of soil cover across the studied region. Climate has a strong interaction with landform factors (such as slope, aspect and altitude) and significantly affects plant growth and soil formation (Jenny, 1941). Recently, Coggins et al. (2019) incorporated 10 soil variables, six terrain variables and three climate variables to define and map six terron classes in the Lower Hunter Valley again. They found that the addition of climate variables and new soil data improved the previously created terron map by Malone et al. (2014). As in the Lower Hunter Valley, the availability of soil information at national scale is the main consideration for establishing a Danish terron system. The Danish soil profile database is a good source of information for this purpose. The database is a product of several decades of soil surveys across the country and therefore represents all Danish soils. It has already been applied to operational DSM in Denmark at different scales (Adhikari et al., 2013; Adhikari et al., 2014b; Peng et al., 2015). However, there are some constraints for the use of legacy soil data (such as soil profile data) for terron identification. If the first procedure is to quantify the differences between soil profiles according to parent material, organic matter, biological properties, etc. (Carré and McBratney, 2005; Minasny and McBratney, 2007), then data availability for each soil profile horizon becomes a major issue. For example, in Denmark, soil samples are traditionally considered as organic soils if the soil organic carbon (SOC) content is greater than 6%. These organic samples were not analysed for soil texture, and nearly 20% of soil profiles thus lack soil texture information in different horizons. Therefore, the number of soil profiles that can be used for establishing a national terron system is very limited if several soil properties are included for terron identification as this requires complete soil attribute information for each horizon in the profile. Otherwise, the entire profile has to be excluded, even if the remaining horizons have full soil attributes information available. Obviously, this soil information is too valuable to be left out. Furthermore, biases from different sampling dates and methods are an important issue to consider when using legacy soil data (Gomez et al., 2016). To ensure a coherent dataset from all soil profiles, we decided to use the Danish national soil Vis-NIR spectral library as soil input data for terron identification, because soil spectra encode information on the inherent composition of the soil, the spectra comprise simultaneously soil chemical, physical and biological information (Viscarra Rossel et al., 2016). To a certain degree, soil spectra behave differently, and these differences are mainly driven by distinct soil characteristics, such as their particle size distribution, mineralogy and chemical properties (Vasques et al., 2014). Therefore, with the differences in spectra behaviour, use of spectral data can be an effective approach to discriminate soil samples and profiles in relation to pedogenetic processes and soil forming factors (Demattê and da Silva Terra, 2014; Terra et al., 2018). The main objective of this work was to assemble various environmental factors (i.e. soil, terrain and climate) to identify and map terrons in Denmark. The implementation consisted of three steps. First, we measured taxonomic distances between reference soil profiles and the Danish soil profile database using the Danish Vis-NIR spectral library. Second, we used soil, terrain and climate factors to identify terrons through a non-hierarchical fuzzy clustering algorithm. Last, we mapped terrons across the country using pre-existing soil maps.

terroir is a distinctive quality sign and a competitive marketing tool that can have major impact on the farmers’ income (Barham, 2003). In 1986, the Food and Agricultural Organization of the United Nations (FAO) set up a database program called “Soil and Terrain” (SOTER). The aim was to develop a global database composed of a polygon map of SOTER units and a set of tables with terrain and soil data (Oldeman and van Engelen, 1993). This database can be applied in areas such as food productions, environmental impacts and conservation. The system was tested in South America and has also been used extensively in Kenya, Central Europe and other regions as a functional tool in regional land management (Oldeman and van Engelen, 1993). However, it is difficult to secure and maintain a perfect register when overlaying polygon layers, because overlay analyses in geographic information systems (GIS) do not always allow identification of the best site amongst those that meet the criteria. As an alternative to polygon maps, researchers can use raster-based high-spatial-resolution environmental information such as finely resolved elevation models, together with advanced digital soil mapping (DSM) techniques. This makes it possible to more accurately describe and locate areas that share similar soil and landscape features and allow for better soil and environmental management practices (Coggins et al., 2019; Malone et al., 2014). Based on previous studies, both mechanistic (Bonfante et al., 2011) and empirical (Carey et al., 2009) approaches for establishing terroir units are generally implemented in two steps. The initial step is generally landscape classification into relatively homogeneous areas that share similar environmental features, such as soil, landform, geology, climate combinations and their interactions (Bonfante et al., 2011; Carey et al., 2009; Coggins et al., 2019; Malone et al., 2014; Priori et al., 2014). These landscape classifications are subsequently referred to as terrons. Terron is a soil-landscape entity, which combines soil and landscape at the same time, and was initially proposed by Carré and McBratney (2005). The major difference between terroir and terron is that the terroir describes both environmental and social factors and the terron concept only comprises soil knowledge, landscape information, associated climatic elements and their interactions. The concept is comparable to agroecozones (Liu and Samal, 2002) and soilscapes (Lagacherie et al., 2001) with respect to management. The advantage of terron mapping is the continuity of soil cover that can be described quantitatively and formally in terms of soil, landscape and climatic attributes. It can serve as a preliminary step for identifying terroirs at multiple scales by incorporating social-economy factors. The terron concept can also be further tested to investigate whether the terron classes give better predictions of landscape-dynamic processes and allow better management options than soil alone. The first terron map was produced by Carré and McBratney (2005) in the La Rochelle area in France. They utilized non-hierarchical clustering methods to establish 18 terron classes (the same number of soil types within the study area) from a set of 1113 observation sites over an area measuring approximately 1054 km2. The results showed that the terron map was more landform-oriented and modelling errors were smaller when compared to a soil map of the study area. On the other hand, the lack at the time of high-resolution environmental information from sensing technologies was a limitation. The terron model could have been improved in terms of both soil characteristics and landscape information if more proximal or remote sensing data were included (Malone et al., 2014). For instance, the chemical, physical and mineralogical composition of soil information can be effectively characterized by soil spectra from Visible Near Infrared spectroscopy (Vis-NIR) (Cécillon et al., 2009; Demattê et al., 2004; Peng et al., 2013; Peng et al., 2014; Peng et al., 2015; Rossel et al., 2006; Stenberg et al., 2010). The second terron study was carried out by Malone et al. (2014) in the Lower Hunter Valley (approximately 220 km2), Australia. The determination of an appropriate number of terron classes also followed the number of soil types within the study area. They noted that the 2

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2. Material and methods 2.1. Data 2.1.1. Study area The study area is Denmark, a North European country with a total area of approximately 43,000 km2. Topographically, this country is relatively flat and smooth, having a mean elevation of 31 m above sea level, with the highest natural point at 170-m. Denmark has a temperate climate, characterized by mild winters, with mean temperatures of 1.5 °C in January, and cool summers, with a mean temperature of 17.2 °C in August. Denmark has an average of 179 days per year with precipitation. Annual precipitation varies from 500 mm in the east to 800 mm in the west of the country, and autumn generally is the wettest season and spring the driest. The weather is very changeable and there are seasonal variations in daylight with short days (7–10 h of light) during the winter and long days (15–18 h of light) during summer (Wang, 2013). In Denmark, more than 60% of the land is used for agriculture and the main crops are wheat, maize, potatoes and barley. Natural vegetation covers 16% of the area, and urban areas only account for 10% of the land area. The island of Bornholm was excluded from the study, due to the lack of climate stations and soil profile data in this area. The formation and distribution of soils in Denmark are strongly influenced by multiple glaciations during the Weichselian geological period, which has resulted in complex glacial land systems across the country (Jacobsen, 1984). Loamy Weichselian moraines are often found in the eastern part of the country, while the western part of Denmark mainly has sandy glacial outwash plains and Saalian moraines, where older and strongly eroded landscapes are common. The northern parts consists of marine sediments mixed with fine sandy materials on post and late-glacial marine deposits (Jacobsen, 1984). On the basis of the Danish soil profile database and previous classification efforts, Danish soils have been classified and mapped into nine soil groups according to the revised FAO–Unesco Legend 1990 (Breuning-Madsen and Jensen, 1996), namely Alisols, Arenosols, Cambisols, Fluvisols, Gleysols, Luvisols, Histosols, Podzols and Podzoluvisols (Adhikari et al., 2014b). The Luvisols and Podzols are dominant soil types in the eastern and western parts of the country, respectively (Adhikari et al., 2014b).

Fig. 1. National 7-km soil grid sampling locations in the study area and locations of Danish reference soil profiles.

2500 nm with a resolution of 0.5 nm, giving 4200 data points. The detailed NIR instrument and spectral data collecting procedure can be found in Peng et al. (2014). A total of 3395 spectra were obtained from these 586 soil profiles. All spectral measurements were standardized to four depth intervals (depth 1: 0–30 cm; depth 2: 30–60 cm; depth 3: 60–100 cm; depth 4: 100–200 cm) to calculate taxonomic distance. If there were two or more spectra in one depth interval, the spectra were averaged. Therefore, each profile only consists of four standardized spectra to represent four different depth intervals, giving a total of 2344 spectra to be used in this study to represent soil factors (i.e. four depth intervals × 586 profiles = 2344 spectra).

2.1.2. Soil profile database A total of 586 soil profiles based on the 7-km national grid (Madsen et al., 1992) were used in the present study (Fig. 1). Sampling work was carried out during 1987–1989. Each profile was dug to a depth of 2 m, photographed and described according to the Danish guidelines for soil profile description (Madsen and Jensen, 1985), which are an adaptation of the FAO (1977) guidelines for soil profile description. All profiles were described and sampled according to the genetic horizon sequence, and the number of sampled horizons varied from three to seven per profile. Some deep horizons were sampled twice. For example, even though one profile had only three genetic horizons (0–30, 30–60, 60–200 cm), the bottom horizon had been sampled twice (70–80 and 180–190 cm). Most soil samples were analysed for soil organic matter (SOM) and soil texture following the Danish national soil analysis standard protocol (Sundberg and Greve, 1999). More detailed information about Danish profile observations can be found in Adhikari et al. (2014b). Additionally, among these 586 profiles, 34 profiles were selected as Danish reference soil profiles Sundberg and Greve (1999). The 34 reference profiles, which are included in the 586 profiles, were selected based on availability of spectral data, differences in parent materials, water availability classes and nutrient supply classes (Fig. 1).

2.1.2.2. Terrain and climate data. In addition to soil factors, four preexisting terrain and four climate data layers were used to identify the terron at each soil profile location. The selection of climate variables for terron mapping relies on the assumption that all included climate factors strongly influence crop growth. Accordingly, we also used only four terrain maps, as landform factors would otherwise be over weighted relative to climatic information. All terrain maps were derived from a 30.4 m national DEM. Based on previous studies (Adhikari et al., 2014a; Adhikari et al., 2013; Adhikari et al., 2014b; Beucher et al., 2017; Guo et al., 2019; Møller et al., 2018; Møller et al., 2017; Møller et al., 2019), four terrain derivatives, namely relative slope position, valley depth, valley bottom flatness and vertical distance to the channel network (Fig. 2 A-D), were designated as significant contributors to the mapping of Danish soils. The four climatic variables were: number of frost days (below 0 °C) in the periods 1–15 April and 1–15 October (beginning and end of growing season), annual number of growing days above 10 °C, global solar radiation during the growing season, and precipitation (mm)

2.1.2.1. Vis-NIR spectral data. In order to develop a national soil VisNIR spectral library, a DS 2500 Lab spectrometer (FOSS, Hillerød, Denmark) was used to scan all soil samples from these 586 soil profiles during the years 2013–2014. The spectral range was between 400 and 3

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Precipitation used monthly averages for 1961–1990 in a 40-km grid (Scharling, 2000). Finally, all maps were resampled to a resolution of 30.4 m (Fig. 2E-H) using ‘bilinear resampling’ in ArcGIS. More detailed climate maps can be found in AGROclimate (2018). 2.1.2.3. Environmental covariates for terron mapping. The last step of this study was to map terrons across the country based on Cubist regression rules between continuous terron memberships and other covariates (i.e. pre-existing soil maps). The selection of covariates for building regression rules was based on: (1) effectively using the soil data that have already been mapped and evaluated within available pre-existing soil maps, and (2) achieving a reasonable cross-section of attributes describing various soil and terrain information. Therefore, a list of pre-existing soil maps (30.4-m resolution) used in the present study is shown in Table 1 and Fig. 4. 2.2. Method 2.2.1. Taxonomic distance calculation The Mahalanobis distance (Mahalanobis, 1936) is commonly used for spectral discrimination, and it is based on the covariance among variables in the feature vectors which are compared. Therefore, the taxonomic distances between each corresponding depth intervals (i.e., 0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) were calculated and resulted in four taxonomic distance values between two profiles. Then the taxonomic distances between 586 profiles and 34 reference profiles were calculated as the mean distance of four depth intervals using Mahalanobis distance, resulting in the taxonomic distance matrix Dir (i = 1 to 586, r = l to 34). Then, each of the 586 soil profile locations i was described as the distance to each of the 34 reference profiles r. To avoid soil factors being unduly weighted relative to terrain and climatic factors, the dimensions of this matrix were reduced by using a Principal Component Analysis (PCA). The number of components n was set to contain at least 95% of the variance in the data. The taxonomic distance matrix Dir was then transformed into an n- principal component score value matrix Sin (i = 1 to 586, n = l to n number of components).

Fig. 2. Maps of the four terrain derivatives and four climate parameters for terron identification in Denmark. Reading from map A to H: Vertical distance to the channel network, Valley depth, Relative slope position, Valley bottom flatness, Annual Precipitation from 01/04 to 31/10, Average solar radiation from 01/04 to 31/10, Frost days: average number of days below 0 °C from 1 to 15 April and 1–15 October, Growing days: average number of days above 10 °C during a year.

2.2.2. Combination of climate and terrain factors All climate and terrain values for the soil profile locations were extracted from the raster maps, and all the data were scaled between 0 and 1 for further analysis. This resulted in a matrix Eie (i = 1 to 586, e = 1 to 8). For each of the 586 soil profile location i was described by four climatic factors (frost, growing days, solar radiation and precipitation) and four terrain derivatives (relative slope position, valley depth, valley bottom flatness and vertical distant to the channel

during the growing season (Fig. 2 E-H). Temperature and solar radiation data were accessed from the meteorological database of Aarhus University (AGROclimate, 2018). Daily mean and minimum temperatures across the country from 1985 to 2013 were recorded in a 40-km grid and measured in Celsius at 2 m above ground level and global solar radiation in MJ m−2. Kriging was performed on the 40-km grid to generate maps of frost, growing days, and global solar radiation.

Fig. 3. Flowchart summarizing the soil, terrain and climate data integration for the terron identification process across the study area. 4

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Table 1 Environmental covariates for prediction of terrons in Denmark. Environmental variables

Type of variable

Description

Reference

SOC map Clay map (< 0.002 mm) Silt map (0.002–0.02 mm) Fine sand map (0.02–0.2 mm) Coarse sand map (0.2–2 mm) Drainage index Geology Landscape Landuse

Continuous Continuous Continuous Continuous Continuous Continuous Categorical Categorical Categorical

Averaged 6 maps from different depths (0–2 m) (resolution: 30.4 m) Averaged 6 maps from different depths (0–2 m) (resolution: 30.4 m) Averaged 6 maps from different depths (0–2 m) (resolution: 30.4 m) Averaged 6 maps from different depths (0–2 m) (resolution: 30.4 m) Averaged 6 maps from different depths (0–2 m) (resolution: 30.4 m) National drainage map (resolution: 30.4 m) Derived from the scanned and registered geological map (1:25,000) Landform type (1:100,000) CORINE land cover data adopted in Denmark (30.4 m)

(Adhikari et al., 2014a) (Adhikari et al., 2013) (Adhikari et al., 2013) (Adhikari et al., 2013) (Adhikari et al., 2013) (Møller et al., 2017) – – –

(i = 1 to 586, l = l to n + n) was then generated, and each soil profile location i was described as a combination of the soil, terrain and climatic factors. Afterwards, the fuzzy c-means algorithm (De Gruijter and McBratney, 1988; McBratney and De Gruijter, 1992) was used in order to define nine terron centroids and compare with nine soil types mapped in Denmark. Fuzzy c-means algorithm is a form of clustering in which each data point can belong to more than one cluster. First, the user has to set up a number of clusters to define; afterwards, the algorithm will assign coefficients randomly to each data point for being in the clusters; then repeat until the algorithm has converged in order to find the centroid for each cluster and compute each data point’s coefficients of being in the clusters. At last, the algorithm will assign a membership value to each of the data point. These membership values indicate the degree to which data points belong to each cluster. Thus, points on the edge of a cluster, with lower membership values, may be in the cluster to a lesser degree than points in the center of the cluster (Bezdek, 2013). Regarding the optimal number of terrons to choose, previous studies surmised that the more terrons identified, the more complex the map will be, together with an increased difficulty in differentiating between them (Malone et al., 2014). With the available soil and landscape information in this study, nine terrons may be optimal. Therefore, each soil profile location i was then characterized by membership to the nine terron class centroids. Finally, all 586 soil profile locations were identified as part of a specific terron according to the highest membership value. The fuzzy c-means algorithm was executed from the fuzme R package (Minasny and McBratney, 2002), based on the stand-alone FuzMe software (Minasny and McBratney, 2002). Details of the terron identification procedure can be found in the flowchart in Fig. 3. Fig. 4. Maps of the environmental covariates for prediction of terrons in Denmark. Reading from map A to H: average SOC, clay, silt and total sand contents across four depths, drainage indices, land use, geology and landscape elements).

2.2.4. Terron mapping In order to map terrons for the whole country, the membership for each of the nine terron centroids was predicted for each pixel location. This was carried out using Cubist regression tree models (Quinlan, 1992) and all available covariates (Table 1 and Fig. 4). The modelling process was checked internally, following the ten-fold leave-one-groupout cross-validation approach. Then, nine raster maps were produced to describe the membership (from 0 to 1) of each pixel location to each of the nine terron centroids. The final Danish terron map was determined on a cell-by-cell basis using the terron with the highest membership value.

network). For the 40-km grid meteorological database, the climate information would not show as much spatial variation as the terrain derivatives and 7-km grid soil profile database, and the clustering analysis for terron identification could be biased if climatic variables are used as independent inputs. Therefore, PCA was applied on matrix Eie to address a climate-terrain covariation and enhance the spatial variation of climatic information. The number of components n was chosen according to PCA results from the previous step. Therefore, the climate and terrain information attributes were transformed into the n- principal component score value matrix Cin (i = 1 to 586, n = l to n number of components), the same dimension as the final number of components Sin as for the soil spectra.

3. Results and discussion 3.1. Exploratory soil profile data analysis Descriptive statistics (Table 2) showed that soils in Denmark are heterogeneous with large variations in both SOC and soil texture. The SOC concentrations had a highly skewed distribution. Out of 2344 soil samples, 1700 samples had SOC values below 1%. The variation is mainly because SOC contents are generally higher in the topsoil (0–30 cm), wetland/peat soils have very high organic carbon contents

2.2.3. Terron identification using soil, terrain and climatic factors For each soil profile location i, the climate and terrain principal component score value matrix Cin was added to the taxonomic distance principal component score value matrix Sin. A new combined matrix Mil 5

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weaker peak appears in the blue band region around 500 nm, which mainly represents Fe-oxides such as hematite (490 nm) and ferrihydrite minerals (Knadel et al., 2013). Strong water effects can also be found around 1400 and 1900 nm. This is mainly due to water incorporated into the lattice of some clay minerals that absorb strongly near 1400 and 1900 nm, and is directly related to the mineralogy of the samples (Hunt, 1977). The absorption values in all four-depth spectra are generally increasing between 2200 nm and 2500 nm, which can be described as the major mineral diagnostic region and contains information on carbonates and different types of clay minerals such as smectites and illite. This is because clay mineral absorptions are mostly due to OH, H2O, and CO3 overtones and combination vibrations of fundamentals that occur at longer wavelengths in the mid-IR region (Stenberg et al., 2010). Of the four average absorbance spectra, the depth 1 spectrum (0–30 cm) had the highest absorption values, particularly in the visible region. This can be explained by topsoils generally being darker than subsoils due to the differences in organic matter content. Information on soil colour can be extracted from the visible part of the spectral range and correlates strongly with SOC content and iron content (Rossel et al., 2008). Furthermore, SOC is spectrally active throughout the spectrum at different depths as a result of overtones and combination bands of CH and CO groups. Therefore, as shown in Fig. 5, average soil spectra from deeper horizons generally present low absorption values caused by relatively low organic matter contents and higher sand contents. This agrees with previous findings in Denmark and other countries (Peng et al., 2014; Stenberg et al., 2010; Viscarra Rossel et al., 2016). With the exception of depth 1 (Fig. 5a), the average spectra for the remaining depths 2–4 showed clay mineral characteristics and carbonates with broad absorption near 2200 and 2340 nm. This could be attributed to micaceous minerals such as smectitic clays. Topsoils generally contain less minerals than the soils from deeper horizons (Schoeneberger et al., 2012). As a result, topsoil spectra have pronounced weaker absorptions in this region compared with spectra from the subsoil. Particularly spectra from depth 4 (100–200 cm) showed sharper peaks in this region, strongly signaling mineral soils. This can be explained by smectite being the dominant clay mineral in the clay fraction in most Danish soils (Moberg, 1990). It is important to note that different types of clay minerals can result in spectra behaving differently, even when two samples have the same percentage clay content as determined by wet chemistry analysis. Therefore, soil spectra have a great advantage as input data for terron identification in providing information on parent material, geology and associated biochemical and physical characteristics. This advantage is in line with the terron study carried out in Australia by Malone et al. (2014).

Table 2 Characteristics of the 586 soil profiles (0–200 cm) in the study area. Soil properties (%)

Min

Max

Mean

Median

SDa

Q1b

Q3c

SOC Clay Silt Sand

0.01 0.1 0.5 11.8

56.2 72.3 89.1 97.3

1.1 10.6 19.8 80.3

0.3 7.7 20.6 82.9

3.4 8.79 13.9 14.8

0.12 3.6 6.7 70.6

1.1 15.9 28.8 92.9

a b c

Standard deviation. The median of the first half of the dataset. The median of the second half of the dataset.

Fig. 5. Averaged absorbance spectra (black curve) by depth and their corresponding standard deviations in grey (a, b, c, d). PCA score plots for PC1 versus PC2 for the national soil profile database spectral library by depth and 34 national reference soil profiles by depth (in e, f, g, h: black dots representing the national soil profile database, and red dots the 34 national reference soil profiles).

3.3. Danish reference soil profile spectral information The first two principal components (PCs) for the absorbance spectra of the Danish national soil profile explained around 90% of the total variance at each depth. The score plots of the first two PCs (Fig. 5e-h) were shown in two groups representing the national soil profile database (black dots) and the reference soil profiles (red dots) (Sundberg and Greve, 1999). Due to a large variation in the soil characteristics in the Danish soil profile database (Table 2), the national database also showed larger variations than the reference soil profiles in the PC space for all four depths. There were a few outlying samples with extreme score values. These samples were carefully checked and found to be either organic soils from wetlands or very clay-rich soils from marshlands. This finding also confirmed that the first two PCs were highly associated with SOC and clay content (Knadel et al., 2013). More importantly, within the four score plots (Fig. 5e-h), the reference soil profiles (red dots) showed similar patterns to the national soil profiles (black dots) at each depth, indicating that Danish soils were generally

(Adhikari et al., 2014a) and because Danish soils have high sand contents (mean values of clay, silt, sand contents were 10.6, 19.8 and 80.3%, respectively). Western and northern parts of the country have a higher sand content compared to the rest of the country, whereas the soils in the eastern and central parts of the country are richer in clay (Fig. 4B & D).

3.2. Soil spectral data The distribution of soil spectra used in this study is shown in Fig. 5 (a-d). All spectra have a similar general form with absorbance wavelength slightly increasing in the blue band range (400–500 nm), decreasing steeply thereafter. In all four-depth spectra, the absorbance values are generally higher in the visible region (400–780 nm) than in the infrared region. A 6

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Table 3 Loading values of the four reference soil profiles, taxonomic distances for the three first principal components (PC) and associated soil properties. PC1

PC2

PC3

Depth

SOC (%)

Clay (%)

Fine silt (%)

Coarse silt (%)

Very fine sand (%)

Fine sand (%)

Medium sand (%)

Coarse sand (%)

Profile 1

0.44

−0.43

0.31

Profile 2

0.19

−0.05

0.15

Profile 3

0.14

−0.03

0.03

Profile 4

−0.20

0.27

0.12

1 2 3 4 mean 1 2 3 4 mean 1 2 3 4 mean 1 2 3 4 mean

9.50 3.50 0.29 0.06 3.31 2.82 0.88 0.18 0.12 1.00 1.53 0.29 0.06 0.06 0.49 2.17 0.53 0.18 0.12 0.75

1.50 4.20 3.80 2.10 2.90 11.80 19.20 25.00 12.30 17.08 19.30 18.70 16.40 13.10 16.80 6.60 4.20 6.00 4.60 5.35

0.50 1.20 1.20 0.90 0.95 15.30 12.60 11.40 4.40 10.93 16.60 15.10 18.00 11.80 15.37 11.00 8.80 8.80 10.60 9.8

1.20 1.20 1.20 1.00 1.15 15.80 11.60 13.30 7.70 12.10 18.80 13.90 15.20 13.40 15.32 21.10 38.80 42.10 65.30 41.82

1.50 1.20 1.10 2.30 1.52 12.80 12.20 13.10 8.90 11.75 15.20 16.70 14.30 12.50 14.68 17.70 37.20 35.80 18.80 27.38

3.10 6.40 5.60 8.80 6.00 12.40 11.60 10.20 10.80 11.25 9.40 13.20 10.50 12.70 11.45 11.10 5.20 3.70 0.00 5.00

34.30 80.50 79.20 83.00 69.25 27.80 24.50 20.70 39.00 28.00 16.60 16.00 17.50 27.90 19.50 28.20 6.40 3.50 0.00 9.50

9.70 7.60 7.60 3.00 7.00 8.30 7.80 7.20 18.20 10.38 6.60 5.80 6.90 7.80 6.78 6.00 1.00 0.00 0.00 1.75

Clay < 2 µm, Fine silt: 2–20 µm, Coarse silt: 20–63 µm, Very fine sand: 63–125 µm, Fine sand: 125–200 µm, Medium sand: 200–500 µm, Coarse sand: 500–2000 µm.

and McBratney (2005) did, probably because all profiles were harmonized to four depths in this study. It appeared that PC3 correlated with contents of medium-sized sand. We also found another advantage of using soil spectral information to measure mean taxonomic distances between soil profiles. Soil spectral information is able to minimize effects in relation to similar SOC contents in subsoil, and more efficiently describe the differences between two profiles in this case. This is most likely because a soil spectrum comprises distinct and large amount of soil chemical, physical and biological information at the same time. For example, in Table 3, profile 1 shows large differences in PC1 loading values compared to the other three profiles, even though they have more or less the same SOC content (< 0.3%) at depths 3 and 4 (60–100 cm and 100–200 cm, respectively).

well represented by these 34 reference soil profiles. Particularly for reference soil profiles at depth 1 (Fig. 5a) there was close similarity to the national profile database. 3.4. Taxonomic distances The mean taxonomic distance between 586 profiles and 34 reference profiles resulted in a taxonomic distance matrix Dir (i = 1 to 586, r = l to 34). A PCA analysis on matrix Dir showed that the first five PCs represented over 95% of the variation, thus it is enough to include the five PCs for terron identification. Then, the 34 reference soil profiles component loadings were calculated accordingly. Because of the difficulty of showing the loadings for all 34 reference soil profiles, we only present four reference soil profiles with associated loadings and soil attributes at different depths as an example in Table 3. Weights of the SOC and clay contents are both positively correlated with the first component. Profile 1 presented relatively high positive loading values for PC1. This can be explained by the high SOC contents in this profile. On the other hand, profiles 2 and 3 also presented positive loading values for PC1, in this case probably due to the relatively high clay and moderate SOC contents in both profiles. Profile 4, with its lower contents of both SOC and clay, was duly found to have negative loading values for PC1. The weights of PC2 appeared positive when there were high coarse silt and very fine sand contents, such as in profile 4 (Table 3). Therefore, the second component tends to be particle sizeoriented. We did not find that PC2 was driven by profile depth as Carré

3.5. Climate and terrain factors Fig. 2 shows different climatic information in Denmark. The west of Denmark receives more precipitation than the eastern part of the country. On the other hand, more solar radiation and growing days are found in the east of Denmark than the west. As with the PCA on the distance matrix in the previous section, the score values for the first five PCs, representing 94% of the variation in the climate and terrain information, were also chosen for further analysis. Table 4 shows the Pearson correlation coefficients between different climate and terrain data and the first three PCs for 586 soil profile locations. PC1 was

Table 4 Pearson correlation coefficients between different climate and terrain attributes, and the first three principle component (PC) score values for 586 soil profile locations.

Solar Precipitation Grow days Frost MF RSP Valley depth Vdis PCs1 PCs2 PCs3

Solar

Precipitation

Grow days

Frost

MF

RSP

Valley depth

Vdis

1.00 −0.76 0.51 −0.58 0.14 −0.14 −0.05 −0.09 −0.58 0.67 −0.29

1.00 −0.28 0.22 0.07 −0.02 −0.07 −0.11 0.26 −0.67 0.54

1.00 −0.72 0.05 −0.12 −0.23 −0.12 −0.53 0.60 0.26

1.00 −0.20 0.15 0.21 0.18 0.62 −0.53 −0.28

1.00 −0.58 −0.20 −0.53 −0.67 −0.38 0.23

1.00 −0.17 0.61 0.73 0.45 0.17

1.00 −0.07 0.07 −0.23 −0.02

1.00 0.72 0.48 0.02

MF: valley bottom flatness, Vdis: vertical distance to the channel network, RSP: relative slope position. 7

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area is also defined as glacial flood plains and Saalian moraines with a coarse sandy soil, due to low-relief glaciofluvial sandy sediments. Terron 1 dominates the southwestern part of Jutland (mainland part of Denmark). This area is dominated by a moraine landscape and Podzols, the same as Terron 8. However, soils in Terron 1 (clay: 9.8%, valley depth: 7.2) have a finer texture and higher valley depth than in Terron 8 (clay: 4.8%, valley depth: 4.8) (Table 5). Furthermore, Terron 1 receives the largest amount of precipitation (452.5 mm) and the second-highest solar radiation (3034.9 MJ m−2) of all nine terrons. In the eastern and central parts of Denmark which are dominated by Luvisols (Adhikari et al, 2014b), 64 and 74 soil profile locations were identified as Terron 2 and Terron 9, respectively. Soils in this part of the country are mainly loamy soils on calcareous tills dominated by glacial (Weichselian) morainic landscapes (Fig. 4 H). The highest clay and silt contents, average solar radiation and the lowest average precipitation and frost risk were observed for Terron 2 and Terron 9 (Table 5). The major difference between them was in terrain factors. Terron 2 was mainly found in relatively flat and low elevation areas with a larger valley bottom flatness (7.3) and a lower vertical distance to the channel network (2.6). Terron 2 (3149.5 MJ m−2) also receives slightly more solar radiation and less precipitation than Terron 9 (3134.7 MJ m−2). The remaining Terrons (3 to 7) were mainly found in the northern and eastern part of Jutland, representing the most diverse terron zone in relation to soils, terrain and climate (Table 5). Bothe Terron 3 and Terron 6 show high mean relative slope positions, but Terron 6 has the highest mean vertical distance to the channel network (17.4) and the lowest SOC contents (0.8%) of all terrons. Meanwhile, soils in Terron 3 have higher clay contents than Terron 6. Organic soils were found in Terron 4 (SOC: 4%) and Terron 7 (SOC: 4.6%), but the highest mean valley depth was found in Terron 4 (Table 5). Therefore, Terron 4 most likely represents the valley channel, which contains large amounts of soil organic matter. These results correspond with previous studies, which found that the four terrain derivatives involved in this study were highly related to SOC content (Adhikari et al., 2014a; Guo et al., 2019; Minasny et al., 2013). Terron 5 consists of 79 profile locations, which is the second-highest number of soil profile locations. This terron shows relatively moderate characteristics compared to the rest of the terrons in terms of soil and terrain factors, but not climate. Of terrons 3–7, Terron 5 received the lowest mean solar radiation (3032.8 MJ m−2) and the highest mean precipitation (396.5 mm). The northern and eastern parts of Jutland are obviously the most complex terron area in this study (Fig. 6). This is most likely due to their geological history and climate. The northern part of Jutland was covered by glaciers advancing from Norway during the Weichselian glaciation, while the eastern part of Jutland was partly covered by glaciers advancing from the eastern Baltic Sea (Madsen et al., 1992). Similar results were found in previous soil profile investigations and soil classifications according to the FAO 1990 Legend (Adhikari et al., 2014b; Breuning-Madsen and Jensen, 1996). Furthermore, terrons 3–7 all had a high risk of frost in early growing season. The complexity of the

Fig. 6. Terrons identified for the 586 soil profile locations.

negatively correlated to solar radiation, growing days, and valley bottom flatness, and positively correlated to the remaining factors. On the other hand, PC2 was positively correlated to solar radiation, growing days, relative slope position and vertical distance to the channel network. Although PC3 was negatively correlated to both solar radiation and frost, it was difficult to analyse since it contained the least amount of information. 3.6. Terron identification Fuzzy c-mean clustering was applied to identify nine terrons based on 586 soil profile locations across the country (Fig. 6). Identified Danish terrons and their corresponding mean soil, terrain and climate attributes are shown in Table 5. The terron with the largest number of locations was Terron 8 (86 locations). Terron 8 is mainly located in the western part of the country, which consists mainly of Podzols (Adhikari et al, 2014b) with the highest average sand content (91.9%) and the second-highest average precipitation (441.5 mm). This relatively flat

Table 5 Soil, terrain and climate characteristics (mean value), number of profiles and percentage of mapping area for each identified terron. Terron

Number of profiles

SOC (%)

Clay (%)

Silt (%)

Sand (%)

MF

Vdis

VD

RSP

Solar (MJ m−2)

PP (mm)

GD (day)

Frost (day)

Area in map (%)

1 2 3 4 5 6 7 8 9

77 64 58 47 79 34 67 86 74

1.7 1.5 1.0 4.0 1.5 0.8 4.6 1.9 1.8

9.8 14.8 12.9 8.6 9.4 8.8 6.4 4.8 14.3

6.9 13.9 10.1 7.0 8.3 7.2 5.5 3.2 12.9

83.1 71.1 76.8 84.3 82.1 83.8 87.9 91.9 72.7

3.9 7.3 1.2 3.4 3.6 1.4 6.2 6.2 2.8

2.9 2.6 7.0 4.6 5.8 17.4 2.2 2.3 4.9

7.2 4.4 5.3 19.9 6.7 8.0 7.5 4.8 5.8

0.1 0.1 0.6 0.1 0.3 0.5 0.1 0.1 0.2

3034.9 3149.5 3055.8 3067.4 3032.8 3041.1 3092.4 3014.3 3134.7

452.5 332.9 388.0 368.2 396.5 386.8 373.5 441.5 342.9

153.4 156.0 150.6 149.5 148.9 150.6 148.2 150.2 155.9

3.4 3.0 3.7 3.8 3.7 3.7 3.8 3.7 3.1

11.2 12.8 6.7 5.3 15.2 5.1 9.1 15.4 19.2

MF: valley bottom flatness, Vdis: vertical distance to the channel network, VD: valley depth, RSP: relative slope position, PP: precipitation, GD: growing days. 8

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Fig. 7. a) Map of Danish terrons; b) FAO-Unesco revised soil group map of Denmark Adhikari et al. (2014b).

climate in this area is mainly a result of its location between the North Sea and the Baltic Sea. 3.7. Terron maps Fig. 7a represents the final map of Danish terrons. The area of mapped each terron in Denmark is shown in the last column of Table 5. The largest mapped terron area in Denmark is Terron 9, covering almost 20% of the country. The second-largest is Terron 8, covering 15% of the country. Table 6 shows the cross-validation results from the different terron calibration models and the top three predictors selected by the Cubist calibration models and their attribute usage (in percentage). Of all environmental covariates used for terron mapping (Fig. 4), we found clay and silt provided the highest attribute usage for the model predicting membership of Terron 9, whereas for Terron 8 this was coarse sand. Therefore, mapping of Terron 8 and Terron 9 was in both cases mostly influenced by soil texture. Terrons 1, 2 and 5 covered 11%, 13% and 15% of the country, respectively. Landscape elements and sand contents were the most important variables in models predicting memberships of Terron 1 and Terron 5. Terron 1 is associated with aeolian deposits and Saalian moraine landscapes with fine sandy soils. Clay and silt contents were the two most important factors for mapping membership to Terron 2. Terron 4 and Terron 7 together covered in total around 15% of the country. Both terrons were mainly SOC- and landscape-oriented, but Terron 4 can be described as subglacial tunnel valleys while Terron 7 is mainly associated with post-glacial and late glacial marine deposits. In total, 12% of the study area was mapped as Terron 3 and Terron 6, which are scattered mainly in the northern and central parts of Jutland. Of all nine Cubist models, Terron 3 and Terron 6 showed the lowest accuracy in terms of R2 and root mean square error of cross-

Fig. 8. Map of terron membership value.

validation. Additionally, in attribute usage of both models, none of the covariates showed over 50% relative importance. This indicates that neither terron was closely associated with soil properties, but with terrain information. Both terrons had high mean relative slope positions (Table 5). In future work, terrain information ought to be included as mapping predictors to enhance spatial prediction of some terrons. It is also important to find out the type of terrain information that would be suitable for predicting certain terron classes and to incorporate crop management. 3.8. Terron map uncertainty assessment The final Danish terron map was determined on the highest membership value at each location. Therefore, a terron membership map (Fig. 8) can be considered as an uncertainty indicator for Danish terron map. Lower membership values indicate that the locations do not fall clearly into any terron class and naturally associate with higher uncertainty. In terron membership map, 2% of the study area presents very low membership value (< 0.2), membership value between 0.2 and 0.3 covering 14% of the country. Then, 45% of the terron map

Table 6 The cross-validation results from different terron models, and top three predictors selected by the Cubist calibration models and their attribute usage (in percent). Terron

R2

RMSECV*

Top three predictors and their attribute usage (%)

1 2 3 4 5 6 7 8 9

0.71 0.77 0.56 0.60 0.69 0.51 0.74 0.71 0.65

0.12 0.12 0.14 0.12 0.12 0.15 0.10 0.11 0.12

Fine sand Silt (98%) Landscape (49%) SOC (90%) Fine sand (97%) SOC (46%) SOC (100%) Coarse sand (99%) Silt (100%)

* Root mean square error of cross-validation. 9

Clay (93%) Clay (92%) Silt (44%) Fine sand (84%) Clay (91%) Coarse sand Landscape (86%) Fine sand (84%) Clay (91%)

Landscape (81%) Fine sand (82%) Clay (40%) Silt (67%) Landscape (63%) Clay (35%) Fine sand 82% Landscape (79%) Fine sand (70%)

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In comparison with the soil map, the terron map elaborates how to combine a range of information that is not available from a soil map that only provides information on certain soil attributes. Furthermore, instead of using soil properties from only one depth, the terron map was based on soil profile information from the depth interval 0–200 cm. This has the potential to help authorities and landowners better understand regional-to-site variations in crop production and assess enterprise suitability and rural developments. Our results showed that terrain information should be used as covariates for mapping terrons to enhance spatial information in areas with more complex landforms and pedological processes. Therefore, the choice of using additional terrain derivatives as covariates for terron mapping should be tested in future work. Additionally, due to the constraint imposed on terron mapping when using only coarse-resolution climatic data, capturing micro-climatic zones would be another challenge for terron or terroir mapping, especially for large areas. Finally, due to the limitation of the available soil profile dataset, it was not possible to validate the final terron map with an independent dataset. Perhaps other validation approaches should be considered for terron mapping instead of conventional DSM validation methods. Future work could, for example, validate terron maps by including crop yield information, and delineate nature terroir units depending on the different agriculture applications.

shows moderate membership value (range: 0.3–0.5), and 39% of the country was identified into different terron classes with high confidence (membership value > 0.5). The locations with high membership value (> 0.5) are mainly identified into terron 2, 7 and 8, which associated with soils rich in clay, SOC and sand content (Table.5), respectively. The pixels with low membership value are commonly located in complex geology, landform and climate zones or the area between terron class boundaries. In present study, we decided to have parity between the number of soil classes and the number of terrons to generate as previous studies (Carré and McBratney, 2005; Malone et al., 2014). In future studies, the optimal number of terron in Denmark could be tested together with different crop management application, environmental risk assessment or different terron mapping approach (Coggins et al., 2019; Malone et al., 2014). 3.9. Comparison of the terron map and the soil map In previous study, Carré and McBratney (2005) used a 30% hold-out test dataset to assess the accuracy of the prediction. However, considering the size of the study area and the limited number of soil profiles (5 8 6) used in the present study, this was not a feasible approach for our purpose. In order to assess the accuracy and usefulness of the terron map, we checked its overall quality and consistency by comparing with an existing soil map derived from the same profile database (Adhikari et al., 2014b). In both maps, the borderline between Terron 8 and 9 (Fig. 7a) and Podzols and Luvisols (Fig. 7b) can be easily distinguished. This borderline corresponds to the edge of the ice sheet in eastern Jutland during the last glacial period. In comparison with the soil map, the valley channels are more visible in the terron map, as terrain was important for terron classes, and pre-existing soil maps used for terron mapping in this study were also mainly developed from terrain derivatives (Adhikari et al., 2014a; Adhikari et al., 2013; Adhikari et al., 2014b; Møller et al., 2018; Møller et al., 2017). This confirms results presented by Carré and McBratney (2005). In eastern Denmark, soils were mainly classified as Luvisols. However, this part of the country was clearly split into two terron classes (1 and 8) because they represent different combinations of soil, terrain and climatic factors. For the same reason, the clear difference between eastern and southeastern Denmark was distinguished. In the northern part of the country, the terron map did not show as much variation as the soil map. Probably, this is because terrain and climatic information used in the present study does not adequately capture details in the northern part of the country. Furthermore, due to the limited availability of spectral data, the present study used only 586 soil profiles, whereas 1171 soil profiles were used for mapping soil types (Adhikari et al., 2014b). In future studies on terron identification, it would be useful to add more soil profiles by augmenting the spectral library and using finer resolution climatic data, which is important to catch local variations in specific areas, especially in micro-climatic zones. Furthermore, the terron identification approach developed in this study can be considered as a generic method to generate a European terron map based on European Land Use/Land Cover Area Frame Survey (LUCAS) soil database.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The study was supported by the ProvenanceDK Project with funding from the Danish Innovation Foundation. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.geoderma.2020.114174. References Adhikari, K., Hartemink, A.E., Minasny, B., Kheir, R.B., Greve, M.B., Greve, M.H., 2014a. Digital mapping of soil organic carbon contents and stocks in Denmark. PLoS One 9 (8). Adhikari, K., Kheir, R.B., Greve, M.B., Bocher, P.K., Malone, B.P., Minasny, B., McBratney, A.B., Greve, M.H., 2013. High-resolution 3-D mapping of soil texture in Denmark. Soil Sci. Soc. Am. J. 77 (3), 860–876. Adhikari, K., Minasny, B., Greve, M.B., Greve, M.H., 2014b. Constructing a soil class map of Denmark based on the FAO legend using digital techniques. Geoderma 214, 101–113. AGROclimate, 2018. AGRO Climate: Klimadatabase. Aarhus University. Barham, E., 2003. Translating terroir: the global challenge of French AOC labeling. J. Rural Studies 19 (1), 127–138. Beucher, A., Møller, A.B., Greve, M.H., 2017. Artificial neural networks and decision tree classification for predicting soil drainage classes in Denmark. Geoderma. Bezdek, J.C., 2013. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer Science & Business Media. Bonfante, A., Basile, A., Langella, G., Manna, P., Terribile, F., 2011. A physically oriented approach to analysis and mapping of terroirs. Geoderma 167, 103–117. Bowen, S., Zapata, A.V., 2009. Geographical indications, terroir, and socioeconomic and ecological sustainability: The case of tequila. J. Rural Studies 25 (1), 108–119. Breuning-Madsen, H., Jensen, N.H., 1996. Soil map of Denmark according to the revised FAO legend 1990. Geogr. Tidsskr 96 (1), 51–59. Carey, V., Archer, E., Saayman, D., 2002. Natural terroir units: What are they? How can they help the wine farmer. South Afr. Wineland Mag. 151, 86–88. Carey, V.A., Archer, E., Barbeau, G., Saayman, D., 2009. Viticultural terroirs in stellenbosch, South Africa. Iii. Spatialisation of viticultural and oenological potential for cabernet-sauvignon and sauvignon blanc by means of a preliminary. Model. J Int Sci Vigne Vin 43 (1), 1–12. Carré, F., McBratney, A.B., 2005. Digital terron mapping. Geoderma 128 (3–4), 340–353. Cécillon, L., Barthès, B.G., Gomez, C., Ertlen, D., Génot, V., Hedde, M., Stevens, A., Brun, J.-J., 2009. Assessment and monitoring of soil quality using near-infrared reflectance

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