Carta vocazionale delle aree naturali di sviluppo di <em>Tuber magnatum</em> in Emilia-Romagna (Italia)
10.6092/issn.2531-7342/10832
In questo lavoro abbiamo utilizzato un approccio induttivo e deduttivo per produrre una carta di attitudine per la crescita spontanea di Tuber magnatum (tartufo bianco) (SSGT) nella regione Emilia-Romagna (nord Italia). Questa carta è stata prodotta al fine di identificare gli ambienti in cui è necessario adottare interventi per la protezione di questa specie di tartufo, fortemente minacciata.I passaggi utilizzati per realizzare la carta sono stati: 1) rilevamento e mappatura delle aree attualmente produttive (APA) in due province dell'Emilia-Romagna (Bologna e Modena); 2) identificazione di alcune proprietà morfologiche, climatiche, vegetazionali e pedologiche ritenute importanti per lo sviluppo di T. magnatum; 3) produzione di cartografie digitali che rappresentano il valore (classificato) che ogni proprietà assume in porzioni discrete di territorio; 4) sovrapposizione della carta delle APA con le carte tematiche e selezione delle proprietà che mostrano le relazioni più forti con la presenza di T. magnatum; 5) realizzazione della carta della SSGT, che copre la superficie boscata del territorio collinare e di bassa montagna dell'Emilia-Romagna; 6) valutazione dell'attendibilità della carta della SSGT mediante sovrapposizione (ex-post) della carta delle APA e quella delle aree delle province di Parma e Piacenza (dataset indipendente) in cui è stato amplificato il micelio di T. magnatum nel suolo, utilizzando PCR specifica.Le relazioni trovate mediante l'approccio induttivo (confronto tra proprietà selezionate e frequenza osservata del tartufo) hanno dimostrato la loro efficacia nel prevedere in modo deduttivo le aree con spiccata idoneità per il tartufo bianco.
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Alessandra Zambonelli
Italian Journal of Mycology
land suitability
Map of suitability for the spontaneous growth of <em>Tuber magnatum</em> in Emilia-Romagna (Italy)
Text.Serial.Journal
https://italianmycology.unibo.it/
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Articles
2020-06-19
2020-04-17
2020-02-10
2020-09-28
38-53
Ital J Mycol
Alessandra Zambonelli
2020/06/19
10.6092/issn.2531-7342/10832
38
Dipartimento di Scienze e Tecnologie Agro-Alimentari, University of Bologna
2531-7342
land suitability
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53
Zampieri E, Murat C, Cagnasso M, Bonfante P, Mello A (2010). Soil analysis reveals the presence of an extended mycelial network in a Tuber magnatum truffle ground. FEMS Microbiology Ecology, 71(1):43-49. https://doi.org/10.1111/j.1574-6941.2009.00783.x.
Map of suitability for the spontaneous growth of <em>Tuber magnatum</em> in Emilia-Romagna (Italy)
https://italianmycology.unibo.it/article/download/10832/11166
49
Laruccia et al.                                                               Italian Journal of Mycology 49: 38-53 (2020)

DOI: 10.6092/issn.2531-7342/10832


Research article

                   Map of suitability for the spontaneous growth
                   of Tuber magnatum in Emilia-Romagna (Italy)
 Nicola Laruccia1, Vittorio Marletto2, Pamela Leonardi3, Federico Puliga3, Alessandra Zambonelli3

1 Regione Emilia-Romagna, Servizio programmazione e sviluppo locale integrato, viale della Fiera 8, 40127 Bologna,
Italy
2 Struttura IdroMeteoClima, Arpae Emilia-Romagna, viale Silvani, 6 - 40122 Bologna, Italy
3 Dipartimento di Scienze e Tecnologie Agro-Alimentari, University of Bologna, via Fanin 44, 40127 Bologna, Italy

Corresponding author e-mail: nicola.laruccia@regione.emilia-romagna.it


Article info
Received 17/04/2020; accepted 10/06/2020
DOI: 10.6092/issn.2531-7342/10832

Abstract
In this work we used an inductive and deductive approach to produce the map of Suitability for the Spontaneous Growth
of Tuber magnatum (white truffle) (SSGT) in Emilia-Romagna region (northern Italy). This map was produced in order to
identify the environments where appropriate actions should be applied to protect this threatened truffle species.
The steps used to define the map were: 1) surveying and mapping the actual productive areas (APAs) in two provinces of
Emilia-Romagna (Bologna and Modena); 2) identification of some morphological, climatic, vegetational and pedological
properties related to T. magnatum development; 3) production of digital maps representing the value that each property
assumes in discrete land portions; 4) overlapping the map of APAs with each of the thematic maps and selection of the
properties showing the strongest relationships with the presence of T. magnatum; 5) creating the map of SSGT, covering
the whole hilly area of Emilia Romagna region; 6) assessing the reliability of the SSGT map, by overlapping the APAs
map and the areas of the provinces of Parma and Piacenza where the mycelium of T. magnatum was found using specific
PCR.
The relationships found by means of the inductive approach (comparison between selected properties and observed
frequency of truffle) demonstrated its effectiveness in predicting deductively the areas with distinct suitability for truffle.

Keywords
white truffle; Italy; productive areas; map; soil mycelium detection; land suitability

Introduction

Truffles are the hypogeous fungi belonging to the genus Tuber (Ascomycetes, Pezizales) (Zambonelli
and Bonito, 2012). They live in mycorrhizal association with trees and shrubs in a wide range of
habitats, including subtropical rain forests, temperate forests, boreal forests, floodplains, tree nurseries,
restoration sites and Mediterranean woodlands (Bonito et al., 2010). Their ascomata have the spores
sequestered in a more or less spherical mass and at maturity they have a strong aroma, which is used
to attract animals for their spore dispersion (Trappe et al., 2009). The pleasant and unique aroma
of some Tuber species makes them the most appreciated mushroom (Mello et al., 2006). Tuber
magnatum Picco is the truffle that commands the highest prices, which ranges from 1,000 to 5,000 €/
kg or more, depending on the ascoma size and the season (Zambonelli et al., 2020). It is principally
found in Italy, for this reason it is also known as “the Italian white truffle” (Hall et al., 2007); recently
it was also found in several countries of the Balkan region and in the countries nearby (Bratek et al.,


ISSN 2531-7342 Department of Agricultural and Food Science in partnership with the Italian Mycological Society (UMI).
The journal is hosted and mantained by ABIS-AlmaDL. © 2020 The Authors. This work is licensed under CC BY 4.0
Laruccia et al.                                                  Italian Journal of Mycology 49: 38-53 (2020)

DOI: 10.6092/issn.2531-7342/10832



2007; Riccioni et al., 2016) and, although more sporadically, in the south-east of France (Hall et al.,
1998; http://www.ayme-truffe.com/en/truffes/tuber_magnatum.php) and Switzerland (Büntgen et al.,
2019).
        Currently, little is known about the complex interactions of T. magnatum with the host plant
and its micro-ecological requirements. Consequently, its cultivation is not successful yet, unlike for
the other precious European Tuber species (Tuber melanosporum Vittad., Tuber aestivum Vittad.
and Tuber borchii Vittad.) (Hall et al., 2007). In the absence of reliable strategies for T. magnatum
cultivation, it is important to safeguard the natural productive areas. An Italian ministerial commission
of MIPAAFT (Ministry of Agricultural, Food and Forestry Policies and Tourism) has established a
truffle-sector plan, which includes the inventory of T. magnatum natural productive areas and several
measures for protecting them (https://www.politicheagricole.it/flex/cm/pages/ServeBLOB.php/L/IT/
IDPagina/11100).
        However, mapping all T. magnatum truffières is a difficult task because the truffle hunters keep
the places where T. magnatum is found as jealously guarded secrets passed down from father to son,
generation after generation (Hall et al., 2007). Although several attempts were made to map them
(Tibiletti and Zambonelli, 2000; Zambonelli and Iotti, 2005), these inventories are not complete and
include only the most well-known productive areas or the areas whose productivity has declined in
the last years. Despite molecular methods to extract the DNA from soil and to assess the presence of
DNA of T. magnatum in the soil, or even to quantify it, have been recently perfected (Zampieri et al.,
2010; Iotti et al., 2012), these techniques are too expensive and time consuming to map the presence
of this truffle at regional level. Their use is generally limited to verifying the presence of truffle in a
determined area or to assess the effects of cultivation practices on its development in the soil (Salerni
et al., 2014; Iotti et al., 2014, 2018).
        Emilia-Romagna region [Nomenclature of territorial units for statistics, level 2 (NUTS 2)] is
particularly rich with truffles. All the most precious truffle species that, according to the National
Law N. 753/1985, can be harvested and traded in Italy, are present in Emilia-Romagna (Zambonelli
and Iotti, 2005). Although T. magnatum grows almost everywhere across the region, it is more
widespread in broad-leaved forests of hills and low mountains. In the plain areas it is quite rare,
because ectomycorrhizal plants are present almost only in urban habitats (e.g. parks, road trees) or in
private gardens (Zambonelli and Morara, 1984).
        Emilia-Romagna region is located in the northern part of Italy; it consists of nine provinces
(NUTS 3: Bologna, Ferrara, Forlì-Cesena, Modena, Parma, Piacenza, Ravenna, Reggio Emilia and
Rimini) and covers an area of 22,446 km2. It can be divided into three large environments: the Apennine
range, the Po valley and the Adriatic coast: to the east it is bordered by the Adriatic Sea for about
130 km. Nearly half of the region (48%) consists of plains (minimum elevation -3 m) while 27% is
hilly and 25% mountainous (maximum elevation 2,165 m). Above about 1,000 m altitude, sandstones
and flysches prevail, from which non calcareous soils [Inceptisols, Entisols and Alfisols (Soil Survey
Staff, 2014)] have developped. In the hilly and low mountain areas, more or less stratified rocks,
often deformed by tectonics and generally calcareous (flysches, clays, marls and sandstones), prevail.
In this environment, erosive processes (sheet erosion and landslides) are widespread and rejuvenate
soils [Entisols and Inceptisols (Soil Survey Staff, 2014), generally calcareous]. The alluvial plain was
recently formed and still the floods continue to rejuvenate the soils [Inceptisols, Entisols and Vertisols
(Soil Survey Staff, 2014), generally calcareous (Regione Emilia-Romagna, 1994, 2018)].
        Average annual temperatures of Emilia-Romagna range from about 8 °C on the highest
mountains to about 14-15 °C in the plain. In winter intense frosts are common, even in the plains,
especially in the areas away from the Adriatic Sea, where moreover the highest summer temperatures
occur. Annual total precipitation varies from more than 2,000 mm in the highest mountains to less


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than 600 mm in the inland eastern areas of the Romagna plain. The distribution of rainfall shows a
main peak in autumn and a secondary peak in spring. The summer water deficit reaches its highest
values in the plains and gradually decreases with increasing altitude (Antolini et al., 2017).
       Considering the difficulties and limits of mapping the Actual Productive Areas (APAs), the map
of Land Suitability (FAO, 1976) for the Spontaneous Growth of Tuber magnatum (SSGT) may be
more inclusive in order to identify the areas where the ecological conditions are more suitable for T.
magnatum growth and hence to extend the protective measures to the areas where a high probability
of truffle presence is expected. In this work a hybrid inductive and deductive approach was applied to
create the SSGT map of Emilia-Romagna region.

Material and methods

Georeferenced information and related databases
The maps of T. magnatum APAs for the provinces of Bologna and Modena used in this study were
produced in 2000 and 2003, respectively (Zambonelli unpublished data, Zambonelli and Iotti, 2005).
The APAs were indicated by truffle hunters and surveillance agents during various field inspections
and delineated on a high-scale topographic map (CTR 1:10,000). The APAs were successively
digitalized in ESRI shapefile format.
      The geographic and alphanumeric information relating to the main environmental factors,
used in this study as basic knowledge, was: i) the regional soil map at 1:50,000 scale (Regione
Emilia-Romagna, 2018), which covers 71% of the regional territory, and the soil map at 1:250,000
scale (Regione Emilia-Romagna, 1994), covering the whole regional territory; for both maps the
information on soil properties and their distribution in each map unit is stored in a MS Access
database, consisting of several related tables; ii) a climatic data set of Emilia-Romagna, which
consists of daily records of the main meteorological data, starting from 2001 until 2015, referred to
1,024 regular cells with side of 5 km. This dataset was chosen because it derives from interpolation
of recent data from local weather station and accounts for topography as well as for the urban share
(ERG5 grid, Antolini et al., 2016); iii) the Digital Elevation Model (DEM) of the regional territory
(ESRI Grid format, 10×10 m resolution, available at http://geoportale.regione.emilia-romagna.it/
it/download/prodottiraster); furthermore, many ancillary rasters had previously been derived from
DEM, namely Slope Factor, Aspect, Topographic Position Index, (https://www.arcgis.com/home/
item.html?id=b13b3b40fa3c43d4a23a1a09c5fe96b9), Curvature (http://www.saga-gis.org/saga_
tool_doc/2.2.6/ta_morphometry_0.html), Morphological Uneveness Index (Generali M., personal
communication), Topographical Wetness Index (http://www.saga-gis.org/saga_tool_doc/3.0.0/ta_
hydrology_20.html), Flow Accumulation (http://resources.esri.com/help/9.3/arcgisdesktop/com/gp_
toolref/spatial_analyst_tools/flow_accumulation.htm) iv) the forestry map of Emilia-Romagna region
(1:10,000 scale) (https://ambiente.regione.emilia-romagna.it/it/parchi-natura2000/foreste/quadro-
conoscitivo/sistema-informativo-regionale/cartografia-interattiva-foreste). For each forest type, the
two main wooden species (trees or shrubs) are reported, along with some ancillary information.
      Except for the DEM and the derived rasters, all the above listed digital maps were available in
vector format (ESRI shapefile).

Data processing
All the geographical data, projected and represented in the same Reference System (EPSG:4265),
were managed in a GIS environment (ESRI ArcGis 10.3).
      The maps of APAs, of soils, of climate and of forestry were converted to raster format with
the same cell size (10×10 m) and alignment as the DEM. A filter was applied in order to exclude the


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cells: i) not covered by the forestry map; ii) with elevation ≥ 975 m asl, where T. magnatum in Emilia-
Romagna is absent (Zambonelli and Morara, 1984; Tibiletti and Zambonelli, 2000); iii) falling within
the Po plain, where no topographical analysis could be conducted. The total number of cells analysed
amounted to 51,146,887, corresponding to 5,115 km2.
       Based on the available literature and on the Authors’ personal knowledge on truffle ecology,
some morphological (slope, slope curvature, aspect, Topographic Position Index, Topographic
Wetness Index, Flow Accumulation Index), climatic (Continentality and Summer Water Stress
indexes), vegetational (host plants) and pedological properties (reaction, calcium carbonate content,
texture, drainage class), supposed to affect the presence of T. magnatum, were selected for further
analysis (Zambonelli and Morara, 1984; Tibiletti and Zambonelli, 2000, Zambonelli and Iotti, 2005;
Bragato et al., 2006; Hall et al., 2007). When the selected properties were not directly available, they
were derived from the primary data.
       As far as concerns the climatic indixes, according to data availability, it was only possible to
calculate them for the ERG5 cells, which have a very low geographical resolution (5×5 km). In order
to allow an analysis of climate as detailed as the other factors considered, we developed an original
downscaling procedure to carry the climatic indexes calculated for the ERG5 cells down to the level
of every single 10×10 m cell. The downscaling procedure involved the selection of the cells from the
ERG5 grid falling within, or immediately surrounding, each province. The selected cells had to honour
these requirements: i) average elevation between 30 and 1,400 m; ii) urban share <20%. For each
province and for each of the selected climatic indexes, by means of polynomial regression analysis we
found the local relationship between elevation and index. Known the difference in elevation between
a given 10×10 m cell and the 5x5 km ERG5 cell within which it falls (Δe), the value of a given index
for the 5×5 km ERG5 cell was downloaded to the 10×10 m cell, adding or subtracting a corrective
value (Δi) to the index. Δi was calculated based on the same relationship previously found, but using,
as indipendent variable, Δe instead of the elevation. The sign of Δi depends on the sign of Δe.
       Values for the selected properties were assigned to each cell of the whole dateset.
       As final outcome of data processing in the GIS environment, a dbf table was realized, which
contains, for each cell of the whole dataset, all the proprieties assigned as described above, as well as
the Boolean information on presence/absence of T. magnatum derived from the APAs map, limited to
the subset of 12,752,436 cells falling within the provinces of Bologna and Modena.
       This table was imported in a new MS Access database, where data were processed and analized
(the database is deposited in an Emilia-Romagna Region’s server). The values of continuous properties
were subdivided in discrete classes, the number of which depending mainly on the range of values.
       In the subset covering the provinces of Bologna and Modena, where the APAs maps were
available, alphanumerical elaborations and statistical monovariate analyses were performed in order
to find, for each environmental factor, the properties (single or combined in indexes) showing the
strongest relationships with the observed presence of T. magnatum.
       To assess the strength of the relationship for each property, the observed frequency (OF) of
truffle (see below) was plotted vs the central value of each class or for the value of each index.
Indexes could indeed assume only few discrete values, as they were derived from combinations of
previously classified individual properties. The coefficient of determination (R2) of the value-OF
relationship (usually derived from non-linear regression and expressed as a polynomial equation) was
adopted as indicator of the strength of the property in describing truffle distribution.
       The observed frequency of truffle was calculated as:




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where OFpi is the observed frequency of truffle for value i of property p (as percentage); TApi is the
total area for value i of property p; tApi is the area occupied by truffle (according to APAs map) for
value i of property p.
       The relationships between environmental properties and OF, detected in the provinces of
Bologna and Modena (where APAs maps were available), were subsequently used as predictive tools
to assess the suitability for truffle growth, expressed in terms of expected frequency (EF), in the
whole study area, i.e. in each of the 51,146,887 selected cells. The EF of a cell corresponds to the
expected probability of finding T. magnatum in it. The EF of a cell was calculated as the average of
the expected frequency for each property assigned to the cell:




where EF = overall expected frequency; EFp = expected frequency for property p; Np = number of
properties.
      If for at least one of the pedological, climatic or morphological properties the EFp value resulted
equal to 0, the cell was considered unsuitable for truffle, even if all the other properties were favorable
for the truffle growth.

Soil sampling and DNA extraction
In the provinces of Parma and Piacenza not covered by APA maps, truffle hunters indicated 35 points
(small patches of around 8-10 m2) in various areas where they found ascomata of T. magnatum
(Fig. 1a). These areas were located in 13 municipalities of Piacenza province (24 areas) and in 7
municipalities of Parma province (11 areas). For each productive point, nine soil cores along two
perpendicular lines were taken, using 1.6 cm diameter disposable polyvinyl tubes (Fig. 1b). One
extra soil core was also taken from a nearby agricultural field as control. Soil cores were stored at
4 °C and extracted by breaking the tubes within 24 h from sampling. Soil samples from the same
productive point were thoroughly mixed. Organic (mainly roots) and coarse fragments were removed.
The soil was kept frozen at -80 °C until lyophilization (70 h in a Virtis Benchtop 2-K freeze dryer, SP
Industries) and later grounded and stirred in a mortar, sieved (1 mm mesh), and then stored at 4 °C
until DNA extraction.
      Soil DNA was extracted after Iotti et al. (2012), adapted for 1 g of soil. Crude DNA solutions
were then puriﬁed using the Nucleospin Plant II kit (Macherey-Nagel, Düren, Germany) following
the manufacturer’s instructions. Total DNA was quantiﬁed by means of a NanoDrop ND-1000
Spectrophotometer (Thermo Scientiﬁc) and their quality evaluated by means of an optical density
(OD) 260/280 nm and 260/230.

Assessment of the presence of T. magnatum mycelium in the soil
A nested PCR approach was used to detect T. magnatum mycelium in the soil. The soil DNAs were
firstly amplified with the fungal ITS universal primers ITS1f/ITS2 (White et al., 1990; Gardes and
Bruns, 1993). The mix was composed of 10× buffer (2 ml), 2.5 mM dNTPs (1.5 ml), 10 mM of each
primer (0.5 ml), water (11.6 ml) and Red Taq (Sigma) (1.4 ml). DNA (25 ng) was then added and the
PCR was run with an initial denaturation at 94 °C for 4 min, followed by 30 cycles of 94 °C for 45 s,
50 °C for 45 s and 72 °C for 90 s, with a final extension at 72 °C for 5 min.
       The second PCR round was performed using the specific ITS primer pair for T. magnatum
TmgI-TmgII (Amicucci et al., 1998). The mix contained 10× buffer (2.5 ml), 2.5 mM dNTPs (2 ml),



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Fig. 1 – Ascomata of T. magnatum (a), soil sampling (b).

10 mM of each primer (0.8 ml), water (16.7 ml), Red Taq 1 U (Sigma) (1.2 mL) and DNA template (1
ml of the first PCR product). PCR reactions were performed with the following conditions: 6 min at
94 °C followed by 34 cycles of denaturation at 94 °C for 30 s, annealing at 50 °C for 30 s, extension
at 72 °C for 30 s and a ﬁnal extension at 72 °C for 5 min.
      The PCR were carried out using a SimpliAmp thermal cycler (ThermoFisher) and PCR products
were run on 2% agarose gel and visualized by staining with ethidium bromide.


Results and discussion

Pedological, morphological, climatic and vegetational properties

Soil indexes
Tuber magnatum prefers deep, fresh but well drained, alluvial or colluvial soils, with calcareous and
soft topsoil (Bragato et al., 2006). For this reason, the calcium carbonate content/reaction and the
texture in the first 30 cm, as well as the drainage class, were chosen as the properties supposed to
mostly affect the truffle presence. The soil descriptions linked to the regional soil maps were used as
the sources for this information (Regione Emilia-Romagna, 2018). The drainage class showed a poor
correlation with the OF, while both the reaction/calcium carbonate content and the texture resulted to
be effective in describing the truffle distribution. We found out that the best relationship was achieved
combining in a single soil index these two properties: i) the texture class (Soil Survey Staff, 2004) and
ii) the combination of empirical classes of calcium carbonate content and of reaction. The empirical
classes reflect the actual ranges for soil units described by the regional soil maps.
       Each property has been assigned an empirical coefficient, based on the degree of matching with
truffle requirements (Table 1).




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                  Table 1 - Classes of selected soil proprieties and coefficients, based on the degree of
                  matching with truffle requirements
                  Calcium carbonate content and pH                                           Coefficient
                  Strongly calcareous (total calcium carbonate >10%)                                  1
                  Calcareous (total calcium carbonate 5-10%)                                       0.87
                  Weakly calcareuos (total calcium carbonate 0-5%) and pH 6.3-7.8. This
                                                                                                   0.14
                  class was also assigned to highly variable soils
                  Non calcareous and acid (pH 4.5-6)                                                  0
                  Texture classes (Soil Survey Staff, 2004)                                  Coefficient
                  Sandy Loam                                                                           1
                  Loam                                                                              0.89
                  Sandy Clay Loam, Clay Loam, Silty Clay Loam                                       0.83
                  Silty Clay, Sandy Clay, Clay                                                      0.26
                  Sand, Loamy Sand                                                                     0

       The idoneity index of a soil was calculated adding up the values of the coefficients for the two
considered properties. If a coefficient had a value of 0, the soil index was set to 0, regardless of the
value of the other coefficient. Each soil map unit, due to the low resolution of the available maps,
may consist of several soil units, sometimes with very contrasting properties, so we had to choose
a method to assign an unique value to the map unit. We found that assigning to each map unit the
highest value among the indexes of the individual soil units (after having excluded those occupying
less than 10% of the map unit surface) gave the best relationship with the truffle distribution.
       In Figure 2a the relationship between the soil index and the observed frequency in the provinces
of Bologna and Modena is shown.




              Fig. 2 - Relationship between pedological, climatic and morphological properties and
              observed truffle frequency: soil index (a), new compound continentality index (CI_comp)
              (b), topographic position index (TPI) (c).

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Morphological indexes
All the available morphometric properties (namely slope, slope curvature, aspect, Topographic
Position Index, Topographic Wetness Index, Fow Accumulation Index) were analyzed. As already
stated, T. magnatum prefers fresh, calcareous and very porous, highly aerated soils. Where the highly
interconnected macroporosity allows this hypogeous fungus to “breathe” and its ascomata to grow
in size without physical constraints (Bragato et al., 2004). In the Appennines, these conditions were
expected to occur in micro habitats, located on the north-facing slopes and at the feet of slopes, where
the accumulation of colluvial deposits, transported by the action of gravity and water, “rejuvenate”
the soil, supplying fresh (calcareous) and soft materials.
       Despite expectations, the slope aspect did not show any significative correlation with truffle
distribution, nor taken by itself neither in combination with other properties, such as elevation or slope
factor. The best indication for truffle presence (R2 = 0.9107, with a 5th degree polynomial equation)
was given by the Topographic Position Index (TPI).
       The Topographic Wetness Index (TWI, adopted by Rellini et al., 2011) was not as strictly
correlated to T. magnatum as the TPI. Two different algorithms (Tarboton, 1997) were considered
in calculating the TWI: a) the D-8 model, considering the direction of flow (and consequently the
accumulation of the flow itself) all distributed in the downstream cell towards which there is the
greatest slope (TWI_D8); b) the D-Inf model, considering the flow direction (and consequently the
accumulation of flow itself) distributed continuously towards the downstream cells as a function of
the slope towards each of these (TWI_Dinf). For each cell, the contributing area is calculated adding
the contribution of all the 8 surrounding cells.
       The shape of the curve of truffle frequency vs TWI_D8 showed some unexplained irregularity,
which resulted in a poor reliability of the best regression found (6th degree polynomial equation),
despite a high R2 (0.90). The relationship between truffle frequency and TWI_Dinf showed a very
clear linear trend, but R2 (0.82) was low if compared to the TPI’s one (Fig. 2c).
       The TPI measures the relative topographic position of the central cell as the difference between
its elevation and the mean elevation within a predetermined neighbourhood (in this study, a radium
of 250 m).
       Positive TPI values indicate that the cell has an elevation higher than the average of the
surrounding cells, while negative values indicate a position lower than the average of the latter.
       The highest truffle presence was found in areas with TPI ranging between -50 e -30, which
correspond with the areas localized at the foot of the slopes.
       Although the relationship between the TPI values and the frequency of the truffle follows a
clear and very evident trend, the shape of the regression curve seems to derive from the superposition
of two different curves (Fig. 2c). This trend is suggestive of a complex relationship between TPI and
APAs, worthy of further investigation.
       For this reason we preferred to calculate the expected frequency adopting discrete values for
classes of TPI, rather than using a continuous function (Table 2).

Bioclimatic indexes
Several bioclimatic indexes were used in previous studies to assess the suitability for T. magnatum.
For instance, in Abruzzo region (central Italy) monthly cold stress, monthly water stress, summer
deficit stress, continentality, thermicity index, yearly ombrothermal index, summer ombrothermal
index, potential evapotranspiration were adopted (De Laurentis and Spinelli, 2009).
       In this study, we chose to evaluate only the continentality and the summer water stress indexes
as, taken together, they can be considered representative of the climatic conditions to which the truffle
is particularly sensitive. The continentality is an excellent indicator of the thermal field, as it accounts


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for both the maximum and minimum temperatures during the year: in environments where extreme
heat and/or severe frosts occur the growth of the truffle is hindered (Zambonelli et al., 2012). The
summer water deficit is well known as a condition that strongly affects the possibility of growth or
production of the truffle (Iotti et al., 2018).

                          Table 2 - Observed frequency of truffle in areas with different
                          Topographic Position Index (TPI) in the APAs
                                TPI values             Observed frequency (%)
                                     TPI < -90                     0.0
                               -90 ≥ TPI < -80                     4.8
                               -80 ≥ TPI < -70                     7.5
                               -70 ≥ TPI < -60                     8.3
                               -60 ≥ TPI < -50                    13.0
                               -50 ≥ TPI < -40                    16.5
                               -40 ≥ TPI < -30                    15.7
                               -30 ≥ TPI < -20                    12.2
                               -20 ≥ TPI < -10                     8.7
                               -10 ≥ TPI < 0                       6.5
                                 0 ≥ TPI < 10                      6.0
                                10 ≥ TPI < 20                      5.8
                                20 ≥ TPI < 30                      5.5
                                30 ≥ TPI < 40                      4.8
                                40 ≥ TPI < 50                      3.7
                                50 ≥ TPI < 60                      3.2
                                60 ≥ TPI < 70                      2.2
                                70 ≥ TPI < 80                      0.3
                                80 ≥ TPI < 90                      0.3
                                     TPI ≥ 90                      0.0

      The analysis carried out found that the relationship between the presence of truffles and the
summer stress index was poor, while the relationship with the continentality indexes was strong. The
continentality indexes seek to express the amplitude of the annual temperature oscillation.
      Among the various continentality indexes proposed by Rivas Martinez (2008), the following
were tested:
1. Simple: averaged difference between the mean temperature of the hottest month and the coldest
   one;
2. Amplified: averaged difference between the mean absolute maximum temperatures of the hottest
   month and the mean absolute minimum temperatures of the coldest month.

      We observed that truffle frequency is well correlated with both indexes, but it is more correlated
with the sum of the two. Hence, we assumed this new compound continentality index as the best
climatic indicator (CI_comp).
      The CI_comp values have been grouped in classes and the relationship with the observed truffle
frequency has been formalized by means of a non-linear regression (Fig. 2b).
      In the context of Emilia-Romagna region, the continentality indexes indeed resulted very
effective in differentiating the climatic conditions, with respect to temperature, between the areas of
internal plain (where a really continental climate is recognized, with very high summer temperatures

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and intense winter frosts) from the montaneous areas, where the lower thermic oscillation is due to
altitude and winds effectively removing the thermal inversion conditions that are very common in the
plains, especially in winter. The optimum values are found in hilly and low-mountainous areas of the
Appennine where, in addition, summer water deficit is moderate.

Vegetation indexes
Tuber magnatum is a hypogean mushroom that lives in symbiosis with the roots of some tree plants.
The presence of specimens of these plants is an essential condition for the life of the truffle. The
following plants, recorded in the forestry map of Emilia-Romagna region, were considered as the
symbiotic plants of T. magnatum (Hall et al., 2007): Carpinus betulus L., Corylus avellana L., Ostrya
carpinifolia Scop., Populus alba L. , Populus nigra L., Populus tremula L., Quercus cerris L.,
Quercus frainetto Ten., Quercus ilex L., Quercus petreae (Matt.) Liebl., Quercus pubescens Willd.,
Quercus robur L., Salix alba L., Salix caprea L., Tilia cordata Mill., Tilia platyphyllos Scop. and Tilia
x vulgaris Hayne.
       As expected, forest types in which, according to the forestry map, at least one of the 2 main
species is symbiont show on the average a higher frequency of the truffle. However, the frame is not
at all homogeneous: as can be seen in Table 3, in the woods with downy oak (Q. pubescens) or black
hornbeam (O. carpinifolia) the greatest occurrence of truffle is observed. Conversely, in the woods
with poplars (P. nigra, P. alba, P. tremula), white willow (S. alba), Turkey oak (Q. cerris) and other
species with less diffusion, the frequency is comparable or even lower than the average frequency of
truffle (Table 3).

          Table 3 - Total area occupied by host plants; absolute and relative area occupied by T. magnatum
                                                                Total surface    Sup. with T.
           Species                                                                                 Sup %
                                                                occupied (ha)   magnatum (ha)
           Q. pubescens                                                60,765           5,725          9.4
           Q. cerris                                                  36,349              2,507        6.9
           O. carpinifolia                                            35,148              3,700       10.5
           P. nigra                                                    7,413                303        4.1
           S. alba                                                     5,379                141        2.6
           Quercus spp. (oaks lacking further specifications)          1,039                199       19.1
           P.tremula                                                     940                 31        3.3
           P. alba                                                       744                 49        6.6
           C.avellana                                                    504                 11        2.1
           Q.robur                                                       473                  3        0.6
           C.betulus                                                     246                0.4       0.16
           S. caprea                                                     229               16.5        7.2
           Q. petreae                                                     61               0.17        0.3
           Q. ilex                                                        58                1.1        1.9
           Tilia x vulgaris                                               25                  0          0
           Q. frainetto                                                   23                  2        8.4
           T. platyphyllos                                                14                  0          0
           T. cordata                                                      3               0.02        0.6




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      Differently from the generalization procedure adopted for the other factors considered, the forest
types present in the map have not been assigned an expected frequency that takes into account the
observed frequency for the individual species or for combinations of the two main species reported in
the map legend. Instead, it was decided to assign the observed frequency (Table 3) only to the types
of the map in which a symbiont species is present. The other types were assigned a value of expected
frequency equal to 0.

Realization of the map of SSGT of Emilia-Romagna region
As already stated, to realize the SSGT map for the whole regional territory, the relationships between
observed truffle frequency and environmental properties, found for the provinces of Bologna and
Modena (where APAs maps were available), were applied to the 51,146,887 10×10 m selected cells.
      The overall expected frequency (EF) corresponds to the average of the 4 EFp values (one
value for each of the selected properties). If one or more of these values (with the exception of
the vegetation-dependent one) assumes a value of 0, the corresponding property is considered as a
limiting factor to the point of absolutely preventing the growth of the truffle.
      The Suitability classes were defined based on the EF, as given in Table 4. Unlike the classic
FAO’s approach (FAO, 1976), according to which a certain potential yield corresponds to each class
of suitability, for the peculiarity of the truffle (which is “searched”) it is more significant to express
the suitability of a place in terms of probability to find it (EF) rather than in terms of predicted yield.
In Fig. 3a is reported an example of the resulting SSGT maps.

                     Table 4 - Total area of SSGT classes, expected frequency (EF) and % of
                     the whole study area
                      Suitability class                    EF       Sup. (ha)     Sup. %
                      S1 –Very highly suitable              >10%       7,002         5.5
                      S2 – Highly suitable                 7-10%      60,598        47.5
                      S3 – Moderately suitable              5-7%      42,302        33.2
                      S4 – Marginally suitable              2-5%       3,498         2.7
                      N – Not suitable                       <2%      14,124        11.1

Detection of T. magnatum mycelium in the soil and SSGT map validation
A first attempt to validate the SSGT map was carried out comparing ex-post the SSGT map with the
APAs map available for the provinces of Modena and Bologna (Fig. 3b). The comparison was carried
out overlapping the two maps and calculating the share of each SSGT class where T. magnatum had
been found (according to the APAs map). The results, reported in Table 5, show that the SSGT map
has an excellent capacity to define areas with different attitudes to truffle production.
       Furthermore, the points where the T. magnatum mycelium was found in the provinces of Parma
and Piacenza were also overlapped to the SSGT map. Only for samples 8, 9 and 10 this was not
possible because they were located in flat areas, not covered by the SSGT map (Table 6). These
field points, although deriving from a less systematic survey, are more reliable than the APAs of the
provinces of Bologna and Modena, since in the former the presence of mycelium was verified by
DNA testing.
       As reported in the Table 6, T. magnatum mycelium was detected in most of the soil samples (27
out of 35) collected in the areas of Piacenza and Parma provinces where production of T. magnatum
ascomata in the last two years was referred. T. magnatum mycelium was never amplified in soil
samples collected in the control areas. The field points where ascomata were declared by truffle
hunters not to be found in recent years seem to have lost the presence of T. magnatum mycelium


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(samples 11, 34 and 35). T. magnatum mycelium was not found also in the field points indicated with
high uncertainty by truffle hunters (samples 12, 23 and 24) (Table 6).
       These results confirm that the detection of the presence of T. magnatum DNA in the soil is a
reliable technique to verify the actual productivity of an area. T. magnatum mycelium forms broad
patches in the soil which can extend up to 100 m far from the host plant (Zampieri et al., 2010). In these
patches the ascomata are formed in different positions in subsequent years. However, the mycelial
biomass tends to concentrate in the soil surrounding the fruiting body and has a decreasing trend going
away from this point (Iotti et al., 2014). Moreover, it is subject to seasonal fluctuation depending on
the soil temperature and moisture (Iotti et al., 2018). Prolonged drought or soil disturbance can cause
the disappearance of T. magnatum mycelium, leading to loss of productivity.
       All the points where T. magnatum mycelium was recently found correspond to areas of the
classes S3 (Moderately suitable; 8 areas), S2 (Highly suitable; 15 areas) or S1 (Very highly suitable:
1 area). The low number of samples falling in the S1 class reflects the low percentages of areas of this
class in the provinces of Parma and Piacenza (2%). In these provinces the productivity of T. magnatum
is lower than in the rest of Emilia-Romagna (Zambonelli and Morara, personal communication). The
points in the areas indicated approximately by the truffle hunters, where T. magnatum mycelium was
not found, fall in S4 (Marginally suitable: 2 areas) and N (Not suitable: 1 area) suitability classes. These
areas were probably not correctly indicated by the truffle hunters and have never been productive.




   Fig. 3- Example of the SSGT map (a); the same area of (a) with the APAs map overlapped on the SSGT map (b):
   N – Not suitable S1 –Very highly suitable; S2 – Highly suitable; S3 – Moderately suitable; S4 – Marginally suitable.

                         Table 5 - Area and area % covered by APAs for each SSGT class
                                                  Total area     APAs’ area
                           SSGT class* (EF)                                        %
                                                     (ha)           (ha)
                          S1 ( >10%)                  6,924             1,457      21.0
                          S2 (7-10%)                 60,371             5,803        9.6
                          S3 (5-7%)                  31,023             1,646        5.3
                          S4 (2-5%)                  14,972               322        2.2
                          N ( <2%)                   14,141               126        0.9
                         *S1 –Very highly suitable; S2 – Highly suitable; S3 – Moderately
                         suitable; S4 – Marginally suitable; N – Not suitable


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                  Table 6 - T. magnatum DNA presence in the analyzed soil samples and the corresponding
                  expected frequency (EF) and suitability class in the SSGT map
                                   Presence of T.
                  Soil sample                                   EF                Suitability class
                                    magnatum
                  1                     yes                   6.4                        S3
                  2                     yes                   8.5                        S2
                  3                     yes                   7.7                        S2
                  4                     yes                   7.7                        S2
                  5                     yes                   8.4                        S2
                  6                     no                    8.3                        S2
                  7                     yes                   9.2                        S2
                  8                     yes                    na                        na
                  9                     yes                    na                        na
                  10                    yes                    na                        na
                  11                    no                     8.7                       S2
                  12                    no                     1.9                       N
                  13                    yes                    9.5                       S2
                  14                    yes                    9.5                       S2
                  15                    yes                    7.0                       S3
                  16                    yes                    6.5                       S3
                  17                    yes                    6.6                       S3
                  18                    yes                    7.0                       S2
                  19                    yes                    8.8                       S2
                  20                    yes                    6.5                       S2
                  21                    yes                    8.5                       S2
                  22                    yes                    7.0                       S2
                  23                    no                     4.2                       S4
                  24                    no                     4.8                       S4
                  25                    yes                   10.5                       S1
                  26                    yes                    5.4                       S3
                  27                    yes                    6.0                       S3
                  28                    yes                    6.0                       S3
                  29                    no                     5.2                       S3
                  30                    yes                    7.2                       S2
                  31                    yes                    7.2                       S2
                  32                    yes                    7.2                       S2
                  33                    yes                    6.5                       S3
                  34                    no                     6.5                       S3
                  35                    no                     6.6                       S3



Conclusions

In this work we used an inductive and deductive approach to map SSGT in Emilia-Romagna region.
The relationships found by means of the inductive approach (comparison between selected properties
and observed frequency of truffle) demonstrated to be very effective in predicting deductively the
potentiality outside the surveyed provinces: an excellent correspondence between SSGT map and
presence of truffles was found. The SSGT map can be used as a valid predictive tool.
       It is worth pointing out that the basic information used had variable cartographic resolutions (e.g.
from 10×10 m for the DEM to 5×5 km for the climate grid, which means that the former is 250,000
times more detailed than the latter) and different quality and reliability (due to inherently different

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detection and survey methods). Nevertheless, this work resulted in a very satisfying outcome. The
gap in resolution between the more detailed and the less detailed maps (i.e. DEM and climate grid)
was bridged by a very accurate downscaling operation.
       Further elaborations of the available data should include: a) downscaling of soil maps (which
currently have a low to very low resolution compared to DEM), using morphometry-based algorithms;
b) a multivariate statistical analysis, aimed at exploring the cross-correlations among different factors
and properties. These activities would require a lot of computing power, due to the huge number of
cells and properties to be analysed.
       The method used to produce the SSGT map may be applied in other geographical environments
where the presence of T. magnatum is well known or is presumable, based on environmental
characteristics. Topographic Position Index and continentality were used for the first time as,
respectively, topographic and climatic indicators of the potentiality for T. magnatum development.
The SSGT map allows to identify the environments suitable for the growth of T. magnatum, where
appropriate protection actions should be applied. In fact, the protection of T. magnatum habitats is
indispensable, as this truffle cannot be successfully cultivated yet and since several factors, such as
intensive harvesting, forest degradation and climate change, are threatening the survival of this prized
truffle species.

Acknowledgments

This work was supported by Regione Emilia-Romagna, Assessorato Agricoltura Caccia e Pesca. The
Authors thank Maria Capecchi, Paola Tarocco, Giulio Ercolessi, Marco Pattuelli, Stefano Bassi, Mauro
Generali of Regione Emilia-Romagna and Gabriele Antolini of Arpae for the valuable contributions
provided in the map construction; Nanì Adelina and Enrico Merli of Regione Emilia-Romagna for
the support in the field surveys.
      The Authors also thank the truffle hunter associations who indicated the areas of T. magnatum
production in the provinces of Piacenza and Parma: Associazione Parmense Raccoglitori Tartufo,
Associazione Tartufai Parmensi, A.P.T.P. Associazione Provinciale Tartufai Piacentini, Associazione
Tartufo Nero di Piacenza, Associazione Nazionale Tartufai Italiani Regione Emilia-Romagna.

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