Joint Master's
Degree in
Sustainable
Development
Final Thesis
Comparative analysis of social and ecological aspects of the farms
joining the local organization Paniere Flegreo.
How the countryside cultivates values
Supervisor
Prof. Fabio Pranovi, Ca’ Foscari University
Co-supervisor
Prof. Giuseppe Feola, Utrecht University
Graduand
Caterina Campese
871001
1
Table of contents
Introduction ... 3
The Paniere Flegreo ... 5
Methods and Materials ... 7
Sociological analysis: Social Network Analysis and Quality of relational goods... 7
Soil ecological evaluation ... 12
Data reordering through Principal Component Analysis... 24
Results ... 26
Sociological aspects ... 26
Ecological aspects ... 40
PCA results. Linking social and ecological aspects ... 43
Discussion ... 45
Conclusions ... 47
Acknowledgments: ... 47
Bibliography ... 48
3
Introduction
Our species practice agriculture since, approximately, 10.000 years (Houghton, 1994). During these millenniums, agriculture has constantly evolved, adapting time after time to society needs. Not more than 3 centuries ago people were still used to produce the food they needed. However, after the industrial revolutions, agriculture has been deeply transformed. With the advent of mechanization, land productivity has been infinitively multiplied and, consequently, the rate food-producers/food-consumers has undergone an immense reduction. Since the 50s’ of last century, multinationals were able to distribute and sell worldwide food products and feed billions of people with a relatively small number of employees. The population growth, whose rate increased sensibly after the world wars, has fostered this phenomenon. Indeed, each year there were many more people to feed, but less people employed in agriculture. This trend has eventually brought to agricultural industrialization. The economy rules governing other sectors were being applied to the primary sector too: maximization of the profit, competitiveness and product homologation. The consequences of this transformation have revealed to cause, not only environmental impacts (loss of agricultural landscapes, water pollution, loss of genetic heritage related to local varieties and breeds) and cultural impacts (loss of local knowledge and identity linked to agricultural management), but also damages to our general nutrition, relationships, health and quality of life (García-Llorente et al 2018).
In reaction to this, in the last decades different models of agriculture emerged. There has been a return to the past on many sides (younger employees in agriculture, pesticides and chemicals aversion, organic regimes encouragement), together with new societal consciousness (healthy eating, sustainable production), new sectoral knowledge and regulations (CAP1 guidelines, production certificates).
Thereby, multiple socio-economic changes have been affecting agriculture and rural areas in Europe. Social and environmental outputs have been re-attributed to agricultural areas, exceeding for number and variety the food-commodity-output. This meant a transition from an agriculture-based to a service-based economy (Dessein et al., 2013).
Today, agriculture can be seen as a wide and diverse set of activities, particularly devoted to social finalities (AA.VV., 2008). Its multifunctionality appears to be a fertile environment to cultivate goods and services that people who works or are in contacts with farms, can return to society. This is due, first of all, because of the obvious and necessaire relation between people, animal and plants, which are fundamental parts of the agricultural environment. Secondly, to the obfuscate distinction between the economic institute (the company) and the family who runs it. Indeed, very often these companies work as big families, involving each employee with real care. This is especially true in Italy, where the average farm size is around 8 hectares, according to ISTAT (https://www.istat.it/it/archivio/167401), and so runnable by a little team or a numerous family.
This socio-agro-environment, jointly with the classic agricultural commodity outputs, produces many non-commodity ones (very rare to find in other environments). Among the others: spread of solidarity, trust, proximity, social inclusion/integration idea, positive emotional states, life pedagogy-education, psychology well-being, wellness, environment and landscape quality, quality of life, sympathies, public access to countryside, landscape quality, water (quantity and quality), soil (quantity and quality), air quality, wildlife habitats (biodiversity), greenhouse gases/carbon sequestration/renewable energy, cultural heritage, food quality and food safety (Sen, 1999; Bryden et al., 2011; Contò et al., 2013). For the majority of these outputs there is no market reward, or it is very little. Nonetheless, these services can satisfy some of the most important human needs which reside in our nature and strongly influence our happiness as the true connection with the ecosystem and with the other human beings. Therefore, in order to preserve them and 1 European Common Agricultural Policies
4 guarantee their perseverance in future societies, they must be protected and promoted, which means studying and quantifying these aspects.
The present research proposes a binary approach to analyse and compare social and ecological features of family-run farms, trying to catch some of the above described features. Thus, it focuses on 9 farms belonging to the Paniere Flegreo, a community of producers and voluntary stakeholders located in the Campi Flegrei area (NA), Campania, Italy (Fig. 1,2).
Although many studies focus on soil quality, as many try to describe agriculture multifunctionality or to depict social aspects in local communities, not that many try coordinate these aspects. Therefore, selecting the right indicators to perform this research has been an hard step. The choice fell on biological soil quality indicators for the ecological part and on relational goods indicators for the social one. Soil biological indicators have been prevalently appreciated in the last twenty years for their ability to correctly assess soil maturity, degradation and correlated risks (Parisi, 2004). Instead, the concept of relation good come from the theory of happiness economics. It indicates a relation who emerge and persist on its own, excluding any substantial convenience and that is mainly supported by reciprocity and gratuitousness. Further explanations will be given in the section of Methods and Materials.
Finally, the aim of this study is to explore and evaluate the ecological soil quality in 9 farms joining the local organization Paniere Flegreo in Campi Flegrei (NA), Campania, Italy. At the same time, it evaluates social relations between companies and other institutions, competitors or clients, trying to find out if and how the social environment mirror in the ecological environment
Within this frame, this thesis will try to answer three main research questions:
Are the Soil Biological Quality Indexes, used to analyse soil quality in cultivated Mediterranean areas, showing results in line with the publications investigating Italian cultivated soil quality in the last two decades?
Does the Social Network Analysis enable to detect relational goods between farms and different actors? Does the social environment impact in any ways the ecological environment (and/or vice versa) of the farms joining the local organization Paniere Flegreo?
5
The Paniere Flegreo
With the name of Campi Flegrei, it is indicated the volcanic area, which is extending northwest of Napoli, including the localities of Pozzuoli, Baia, Bacoli, Quarto, Monte di Procida and even farther as Marano and Soccavo. Its volcanic vocation represents at the same time its threat and peculiarity. During the centuries, the zone has been destroyed several times by eruptions, as reported by the ancient Greek and Roman writers, and nevertheless continued to be populated, for its mild climate, landscape beauty and natural prosperity. In fact, the volcanic soil is recognized as highly fertile. Over the time, natural calamities made the population of the zone very resilient but also very ‘diffident’. In the same way, agriculture activities are resilient, not massively productive, but gifted with particular tastes due to the sulphuric notes. Today, the wines from Campi Flegrei are very popular, as well as some ancient varieties of vegetables and legumes (e.g.: Cicerchia flegrea, Lathyrus sativus L.).
Figure 1: Campi Flegrei map; sources: http://campiflegrei.na.it/wp-content/uploads/2013/09/cartina.jpg, https://watchers.news/2016/12/22/campi-flegrei-reawakening-study/
Since 2014, different producers of this area decided to join the Slow Food association 2and create the
so-called “Condotta of Campi Flegrei”. A Condotta is a local organism of Slow Food associates that presides over a specific territory and pursues the protection of local ecotypes under the Slow Food aegis. The Condotta must respect Slow Food principles like promotion of extensive agriculture models, protection of endangered species, taste development and education against food homologation, safeguard of local cuisines. Even if today these principles are still respected and the associates take part to national Slow Food events, very soon after its creation, the Condotta turned into something else. A new dimension of aggregation was coming out.
In the beginning, the producers decided to meet up once a week to organise a little market, usually during the weekend. This market was taking place, in turn, in one of the joining farms. In this way, people started to visit the farms and to frequent more often the area, while buying local and fresh products. During these meetings the group started to call itself Paniere Flegreo, referring to the basket of fresh products from Campi Flegrei. The group was enlarged not only by new producers but also by restaurateurs, loyal consumers, contractors, other stakeholders and assiduous. At that point, the weekend market was not
6 anymore the only activity proposed by the group, but they start to organize events for tasting/promoting the local products and technical meeting for sharing experiences and define new strategies.
Eventually, the group decided to give itself an identity writing a manifesto. The manifesto states the main activities of the Paniere Flegreo group which are: preserving and respecting the Campi Flegrei’s landscape; passing on the native seeds according to traditional uses; respecting the products’ seasonality; guarantying fresh products; encouraging local, short and transparent supply chains; encouraging recycling and re-using processing wastes; respecting the workers giving fair remunerations; preferring recyclable packaging. In some way, we can say that Paniere Flegreo is a local organization which took the moves from an international association, but felt –at a certain point- the urgency to differ its action and model it onto the needs of its territory and its components. Without sever its link with Slow Food, Paniere Flegreo found a way to affirm its identity and continue to grow. Indeed, in peculiar realities, as for Campi Flegrei, local organizations need more flexibility and authority to accommodate internal pressures or sudden changes, continuing to grow and avoiding the drying of its actors’ motivations.
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Methods and Materials
Sociological analysis: Social Network Analysis and Quality of relational goods
Wishing to be able to represent the social mechanisms proper of little agricultural communities and thus detectable in the Campi Flegrei’s farms at hand, a quali-quantitative methodology was adopted. Indeed, the Social Network Analysis (SNA) is a quantitative method, but its discrete nature allows to set it up as a tool able to solve qualitative research as well (de Nooy, 2010). For these purposes, the procedure has been the following: questionnaire administration, Gephi analysis, indexes calculation, relational goods identification.
Questionnaires:
The questionnaire (reported further in Fig. 2 and 3) was built on by exploiting the convergence of the SNA ego-centred perspective and the relational good theory. For the sake of clarity, basic definitions are now provided.
SNA: “It is based on the theoretical constructs of sociology, mathematical foundations of graph theory and recent developments in computer hardware and software, social network analysis (SNA) offers a unique methodology for visualizing and investigating social structures and relations. While a general social survey usually allows for studying individuals’ properties as the prime context for explaining outcome, SNA incorporates the social context to explain individual or group outcomes. The relationships between the actors hence become the focus of study and the properties of the actors themselves remain secondary” (Chung et al 2005).
EGO-SNA: The egocentric approach originated in the late 30’s, from the efforts of a group of anthropologists at the University of Manchester. They studied the networks of relations surrounding individuals rather than focusing on the whole society (Chung et al 2005). Nowadays, in fact, the ego-centred perspective focuses on the composition of local network structure, aiming to answer questions like: “Do actors influence one another through their network ties and/or do actors adjust their ties to the characteristics of their peers and to their ties with them?” (de Nooy, 2010).
RELATIONAL GOODS: Many authors have developed on this notion, which is particularly hard to define, as much as it is embedded and important in our every-day-life. “Despite differences among them, it is agreed that relational goods spring out of interpersonal relationships, as a function of who the people involved are (personalisation), and of what they do, prefer, and feel (in particular, reciprocity of actions and attitudes matter, and non-contractibility prevails); and, therefore, that relational goods are simultaneously created and consumed by interacting parties.” (Gui, Stanca 2010). Probably the aptest definition within this study (and in my opinion) is the one proposed by Martha Nussbaum, a neo-Aristotelian philosopher who was influenced by the thought of Amartya K. Sen and John Stuart Mill: “Relational goods are thus those human experiences in which the relationship itself is the good” (Bruni, 2013).
According to Bruni 2012, Important features that allow to recognize the relational goods are:
-Identity: the identity of the individuals involved is a fundamental ingredient (principle of non-anonymousity)
-Reciprocity: the goods are so only when are enjoyed mutually
-Simultaneity: although the contribution to the production of the meeting may be asymmetric, these goods are simultaneously produced and consumed. Or better, the good is co-produced and co-consumed at the same time by those involved
8 -Motivations: the relation must be genuinely reciprocated, not depending on exogenous interests (e.g.: business)
-Emergent phenomenon: this relation is a 'third' that exceeds the contributions of those involved, which in many cases was not among the initial intentions
-Gratuitousness: the relationship is not 'used' for anything else, if it is lived out as a good in itself, and sources from intrinsic motivations
-Good: the relationship is a good, but it is not a commodity (in Marx's terminology), that is, it has a value (because it satisfies a need), but it does not have a market price (precisely because of gratuitousness), though it always has an opportunity cost.
In order to interpret the questionnaire results and extrapolate whether relational goods are present or not, it is fundamental having in mind these descriptors.
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Figure 2: Questionnaire for in-going relations
10 The analysed nets were depicted on the basis of their collaborations, which were divided in three main categories (highlighted in yellow in Fig. 2 and 3 above): material- economic, knowledge improver, new relations. The first category represents all those relations, within a net, for whom the monetary aspect (compensation/reward/payoff) remains the main. In the same way, the knowledge improver categories embraces all the ties based on a know-how-gaining-intent. New relations is, instead, the category that stands for all the connections that do not belong to the previous categories and seems to be emerged spontaneously.
The questionnaires were written and administered in Italian language, because they were addressed to Italian farmers. The original version is provided in the Appendix. The administration and collection of questionnaires took place from January 2019 to April 2019. I have personally brought the questionnaires to the respondents. In all the companies the administrative chief was the person delegated to answer the questionnaire. I did not set up a time to complete the questionnaire, indeed the procedure was quite unformal. I wanted them to have the time to understand, rethink and then answer. The major part of the respondents, namely the ones from: Masseria Sardo, Giardino dell’Orco, La Sibilla, Costagliola, Paesano and Contrada Salandra farms, during the interview asked for more time. I decided to let them have the time they required, and they returned me the questionnaire completed via email.
Gephi mapping and indexes calculation:
To produce a first network screening, Gephi has been used. It is an open source for network analysis and visualization. The software is available in its last version at this site: https://gephi.org/.
Maps have been produced for all the companies at hand, moreover the maps have been coded using colours to emphasize motivation and multiplexity. Darker the colour (respectively orange/brown for motivation and pink/violet for multiplexity), higher the value of these two indicators.
The motivation coding is based on a rank given by the sum of the four cells highlighted in pink in Figures 2 and 3. The minimum score was set up at 4 (given by 4 answers weighting 1), while the maximum score was set up at 16 (4 answers weighting 4 each). Each answer was indicated by a letter: A, B, C or D. The first two columns were associating the highest scores, which is 4, to D and the lowest, which is 1, to A (D=4, C=3, B=2, A=1). The last two columns were associating the highest score to A and the lowest to D (A=4, B=3, C=2, D=1). In this way, the higher scores were always associated to the gratuitousness motivation.
The multiplexity coding is based on a rank too. This time the rank is given by the number of relation typologies holding the tie between two actors, therefore the maximum score was set up at three and the minimum was set up at one. As mentioned before, the three relation typologies are highlighted in yellow in Figures 2 and 3 and namely they are: material- economic, knowledge improver, new relations. The multiplexity index generally measures the strength of a tie, according to the principle that more typologies or interests are holding a relation, stronger this relation will be. But it is important to remind that this index does not consider which typology may be more relevant or important, evaluating all the typologies as the same. An additional pie-chart representing multiplexity was also proposed, using Excel.
The reciprocity, a fundamental indicator, simply indicates where a tie is mutually hold, thus if a relation possesses both the in-going and the out-going direction. For a relation, in order to be reciprocal, it must be reported in both the two questionnaire sheets (in-going and out-going, Fig. 2 and 3). Visually, reciprocal ties are very easy to identify, indeed in Gephi maps they have a double arrow towards both the actors involved, while non-reciprocal ties show only one arrow toward the receiver.
Finally, the network size index was calculated and represented using Excel. It counts the numbers of relations in a network and it was calculated for each network, and so for each company interviewed.
11 Moreover, sizes were also calculated within each company for each relational category: material- economic, knowledge improver, new relations. This has been useful to understand which relations are the most engaged by farms.
Relational goods identification:
The last step of the sociological analysis has been oriented to the relation goods identification. This has required a careful examination of the data collected through the questionnaires. While the relational goods individuation remains very probably a truly philosophical and subjective decision, in this study 2 main criteria have been selected between the descriptor reported above: the reciprocity and the gratuitousness’ motivation (Paglione 2014,2018). Therefore, only reciprocal ties having a motivation score higher than 8 (so higher than the half of the maximum score) have been taken in account.
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Soil ecological evaluation
In order to analyse the Campi Flegrei’s soil quality and biodiversity, a micro-arthropod oriented approach was chosen. Traditional methodologies for the soil quality evaluation are based on the use of physical, chemical and microbiological indicators. Recently, it has been demonstrated that microarthropods respond sensitively to land management practices and are correlated with beneficial soil functions. Therefore, a new approach based the biological forms of edaphic microarthropods to assess soil biological quality has been proposed (Parisi, 2001). Three indices have been used: the QBS-ar index, the QBS-c index and the Mites/Collembola ratio.
➢ The QBS-ar index is based on microarthropod groups present in a soil sample. Each biological form found in the sample receives a score from 1 to 20 (Eco-Morphological Index, EMI), according to its adaptation to the soil environment. The QBS index sums up these scores, thereby characterising the microarthropod community of the sample being studied (Parisi et al 2017). ➢ The QBS-c only considers collembolan eco-morphological forms by adopting a similar scale with a
different score
➢ The Mites/Collembola indicator (or Acari/Collembola ratio) is an environmental stability indicator (Bachelier, 1978). A decrease in the Acari-to-Collembola ratio is often associated to greater perturbations, such as soil contamination. On one hand, collembola are considered to be more tolerant to contamination than Acari. On the other, Collembola are known to be strongly impacted by the nature and intensity of the agricultural practices employed (Joimel et al 2017). That’s why the A/C ratio is normally greater than one in natural conditions, whereas it decreases in the event of soil degradation (Menta et al 2013).
For each of these parameters the protocol was the following:
Sampling activities
A maximum of five sampling site were collected in each company. Removed the plant cover, the soil was collected using a little standard coring machine which is often use in gardening to plant bulbs (Fig. 4). For each site three areas were sampled and then placed together in the same plastic bag. An area is defined as a representative zone for soil sampling, homogeneous for slope, plant vegetation or use destination (e.g.: common areas). For example, if three sites were identified in the XY farm (common areas, vegetable garden and fruit trees), a total of nine sampling were performed (3x3) and three bags collected. In Table 1 all the companies, sites, dates and geographical coordinates of sampling are reported. Samples were collected when soil moisture was ranging between 40 and 80% of field capacity, so avoiding periods of extreme rains or drought. The sampling activity started on April 17th, 2019 and finished on June 12nd, 2019. Everything
was concluded in the shortest possible time slot, which at the end resulted less than two months. Nonetheless, this may have influenced the results in same way, because the summer beginning in Campania was sudden and extremely warm.
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Figure 4: The sampling gear https://www.bricoferonline.it/Media/img/gardenia-pianta-bulbi-7848.jpg
Table 1: Companies, sites, dates and geographical coordinates of sampling.
Company Site Date Coordinates
Il Giardino dell’Orco
Citrus grove (orange and lemon) 17/04/2019 40.835565 14.071135 Piedirosso vineyard 40.834926 14.070385 Orto urbano 40.835606 14.072386 Common areas 40.835342 14.071866 Veg. Garden 40.835496 14.070999 Masseria Sardo Common areas 18/04/2019 40.8426444 14.0751215
Veg. garden (milled) 40.842541 14.073708
Citrus grove 40.842005 14.074116
Bobobio
Common areas
26/04/2019
40.842129 14.057425
Citrus grove (orange,
clementine, tangerine) 40.842077 14.057789 Veg. Garden 40.842242 14.057711 Sibilla Piedirosso vineyard 09/05/2019 40.815650 14.068562 Falanghina vineyard 40.813049 14.069941 Pisciarella vineyard 40.807721 14.078211 Marsigliese vineyard 40.816941 14.066283 Surecella vineyard 40.812585 14.070659 Salemme Peperoni (veg. g.) 09/05/2019 40.813736 14.068966 Tomato (veg.g.) 40.813732 14.068966
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Green bean (veg.g.) 40.813388 14.069608
KM 0
Tangerine trees
10/05/2019
40.799851 14.059166
Veg. garden (solanaceae in
gh) 40.799588 14.059062
Drupaceae 40.799788 14.058558
Citrus grove (orange and
clementine) 40.799793 14.058740
Common areas 06/06/2019 40.800148 14.059381
Contrada Salandra
Old Piedirosso vineyard
05/06/2019
40.869445 14.075882
Old Falanghina and
beehives 40.869216 14.075866
Falanghina e Greco v. 30
y.o. 40.865089 14.101038
Falanghina e Greco young
v. 40.863575 14.100219 Cabernet young v. 40.865436 14.101155 Costagliola Drupaceae 06/06/2019 40.804180 14.052411 Piedirosso 80 y.o. v. 40.803844 14.052310 Zucchini (veg.g.) 40.804137 14.052497 Falanghina vineyard 40.804080 14.052563 Lemon grove 40.803242 14.053398 Paesano
Veg and fruit garden
12/06/2019
40.889426 14.177335
Chestnut on walkable alley 40.888894 14.177545
Tuff quarry/Hazel 40.887618 14.178345
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Figure 5: Sampling areas map https://www.google.it/maps/@0,-0.0021887,17z/data=!3m1!4b1!4m3!11m2!2sNkQhcNsn4gNANnZZekecHL_xmMFOBA!3e3
Extraction of microarthropods:
The samples were transported into the laboratory which I set up following the instructions described by Parisi (Parisi 2005, 2017). The Berlese–Tullgren funnel has been used for the extraction. This method is simple and cheap, indeed is only composed by an incandescent lamp (40W), placed 25 cm up the soil sample, a sieve (mesh of 2 mm), a funnel, a reclosing container with the fixer liquid (2/3 alcohol 75% and 1/3 glycerol). I added aluminium foils to cover the stations, protecting them from external contacts. In this way also the drying process was facilitated. Pictures of the system are reported in the following figures (Fig. 6-9). The process generated is the following: the incandescent lamp gradually drives the soil present on the sieve, creating an inhospitable condition for the soil fauna, which is then driven into the deeper soil layer, until falling into the container under the funnel, where they remain kept and preserved into the fixer. The soil samples should be put in the Berlese-Tullgren extractor within 48 hours. The duration of the extraction is in relation to the soil moisture, but in general it must never be less than 5 days (Menta et al 2018). In this study all the samples were placed into the funnel during the same day of their extraction, rather than samples collected in Bobobio’s, which waited 12 hours before getting in place. During these hours the samples were taken slightly humidified using a spray and were kept away from thermal shock. All the samples collected stayed into the funnel for 10 days before they were removed.
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20 Specimens setting
The extracted specimens have been observed under a stereomicroscope at low magnification, still into the fixer liquid by using the Petri dishes. During the first step, the specimens were identified and counted, reporting them onto an Excel sheet. After that, the indexes calculation followed.
Figure 10: Soil pedofauna: a, b) Collembola, c) Protura, d) Pauropoda, e) Symphla, f) Diplura, g) Acarina, h) Pseudoscorpionida, i) Litter spider, j) Lumbricina, k) Diplopoda, l) Chilopoda, m) Isopoda, n) Coleoptera larvae, o) Coleoptera, p) Gastropoda
from Community structure and seasonal variations of soil microarthropods during environmental changes, 2018, Cakir, Makineci. Photo by M. Cakir
QBS-ar determination:
The QBS-ar index is based on the biological form identification, which means recognizing the different adaptation levels to soil environment for every systematic group. Within each higher taxon, QBS method requires searching for the biological form (morpho-type) that is most adapted to soil. This type receives an eco-morphological score (EMI), proportionate to its adaptation level. Higher is the adaptation level, higher will be the score assigned. “Eu-edaphic (i.e. deep soil-living) forms get an EMI = 20, hemi-edaphic (i.e. intermediate) forms are given an index rating proportionate to their degree of specialization, while epi-edaphic (surface-living) forms score EMI = 1. Some groups have a single EMI value, e.g.: Protura and Diplura EMI = 20, because all species belonging to these groups show a similar level adaptation to soil. Other groups display a range of EMI values (e.g.: Collembola and Coleoptera), because these groups have species with different soil adaptation levels. Whenever two eco-morphological forms are present in the same group, the final score is determined by the higher EMI. In other words, the most highly adapted microarthropods belonging to a group determine the overall EMI score for that group.” (Parisi et al 2005). In fact, the number of individuals is not relevant for the final score, while the only thing that is taken in account is the presence of each group into the sample.
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Table 2: Eco-Morphologic Indexes (EMIs) for edaphic microarthropods
Group EMI Protura 20 Diplura 20 Collembola 1–20 Microcoryphia 10 Zygentomata 10 Dermaptera 1 Orthoptera 1–20 Embioptera 10 Blattaria 5 Psocoptera 1 Hemiptera 1–10 Thysanoptera 1 Coleoptera 1–20 Hymenoptera 1–5
Diptera (larval stage) 10 Holometabolous (larval stage) 10 Holometabolous (adult) 1 Acari 20 Araneae 1–5 Opiliones 10 Palpigradi 20 Pseudoscorpiones 20 Isopoda 10 Chilopoda 10–20 Diplopoda 10–20 Pauropoda 20 Symphyla 20
Finally, the QBS-ar is simply obtained by summing up the EMIs of all collected groups.
QBS-c determination:
The QBS-c, a specific index for Collembola group, seems to be currently still under validation from the scientific community. However, collembola are among the most abundant soil microarthropods and have shown to be very sensitive to variations in soil environment. Moreover, they show a good variety of morphological features that are easier to detect and to assess (Parisi, 2017), summarized in the following table (Table 3).
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Table 3: A simplified scheme to calculate collembolan’s EMI, from Parisi et al, Agriculture, Ecosystems and Environment 105 (2005) 323–333.
Character EMI score
Clearly epigeous forms: middle to large size, complex pigmentation present, long, well-developed
appendages, well developed visual apparatus (eye spot and eyes)
1
Epigeous forms not related with grass, shrubs or trees well-developed appendages (possible),
developed setae or protective cover of scales, well-developed visual apparatus
2
Small size—though not necessarily—forms, usually limited to litter, with modest pigmentation, average
length of appendages, developed visual apparatus
4
Hemi-edaphic forms with visual apparatus still developed, not elongated appendages, cuticle with
pigmentation
6
Hemi-edaphic forms with reduced number of ommatidia, scarcely developed appendages, often short or absent furca, pigmentation present
8
Eu-edaphic forms with no pigmentation, reduction or absence of ommatidia, furca present—but reduced
10
Clearly eu-edaphic forms: no pigmentation, absent furca, short appendages, presence of typical structures
such as pseudo-oculi, developed postantennal organs (character not necessarily present), apomorphic sensorial structures
20
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Figure 11: Common Collembola in European and Mediterranean soil, From APAT Workshop tematico: Biodiversità dei suoli italiani, Leoni, Menta 2008
According to this specific index, after soil extraction, Collembola are separated into groups. Each group receives a score based on the criteria explained in Table 3. Some of the most common groups in European soils (Fig. 11) are: Podurid, Onychiurid, Isotomid, Entomobryd, Neelid and Sminthurid. For each group, the biological form with the higher EMI value is recorded. The sum of the EMIs for each group, determines the QBS-c Index. As the previous index, also the QBS-c has the advantage to be density-independent.
A/C ratio determination:
The Acari/Collembola index is the ratio given by the number of mites dived by the number of collembola, found in each sample. It was obtained using the Excel sheet already prepared during the specimens count. A low acari density could indicate polluted soil, while a low collembola density could detect stressed soil. Generally, higher is this parameter, higher should be the level of equilibrium between the pedofauna’s communities.
“Le rapport acariens sur collemboles apparaît en relation avec l'équilibre et la stabilité des biotopes” (Bachelier, 1963) “Dans les biotopes en équilibre, où la pression interspécifique est grande, le
pourcentage des collemboles est faible; il augmente en fonction de la dégradation des biocénoses et pourrait être représentatif de l'état d'équilibre d'un milieu”
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Data reordering through Principal Component Analysis
In order to detect the ecological and social interactions, the principal component analysis (PCA) was performed. The PCA is a non-parametric statistical procedure for reducing the dimensionality of data. It can be used to reduce the number of features in a data set by a large factor, if these features are originally correlated. It is important to note that PCA does not reduce features by selecting a subset of the original features. Instead, it creates new, uncorrelated features that are a linear combination of the original ones. More precisely, the original data set is project onto a lower-dimensional system whose axes define new uncorrelated features (Phan, 2016).
In this case, the PCA has been used to reorder the large and mixed amount of data collected (sociological and ecological), reducing its redundancy and easing the focus on the most characterizing data structure. In this way, finding the link between the ecological and the sociological features, became less blurred. The Spearman correlation was performed on the original dataset, in order to make the different units of measure homogenous and comparable.
Geometrically, it is possible to affirm that the PCA operates a reference exchange, maximising the visible information expressed by the data (e.g.: Fig.12)
Figure 13: Effect of applying PCA to a data set. Left: The original data instances have 3 features and so are in 3-D space. Top-right: After applying PCA, the original data points can be reduced to 2 features by projecting them onto a 2-D plane. Bottom-right: The original data can be further reduced to 1 feature on a 1-D line.
From: Thomas Phan Technical Report, 2016: “An Introduction to Principal Component Analysis with Examples in R” Figure 12: Reference exchange on a key and a pen (Marsili Libelli 2013)
25 A series of linear algebra steps allows to compute the principal components:
➢ scaling and re-centring the original dataset
➢ computing a covariance/correlation matrix from the data ➢ computing the eigenvectors and eigenvalues
➢ taking the eigenvectors as principal components
➢ using the eigenvalues as the variance of the data when projected onto the axes defined by the principal components
➢ choosing the final number of principal components.
It must be said that, if on one hand a very small number of principal components (p.c.) would be desirable (reducing the amount of data and so improving its readability), on the other hand, if too many dimensions are removed, the data may not capture important details. The “detailing level” is quantified by the total variance explained by the chosen p.c. Indeed, the first principal component, the most representative data feature, is logically chosen because its maximize the variance (spread) of data onto its axes and, at the same time, minimizes the total projection errors. All subsequent principal components are then chosen to be orthogonal to the previous principal components and solve the variance-maximization-projection-error-minimization problem just mentioned. Generally, it is true that, if the correlation between the original features is high, then a low number of p.c. will be sufficient, instead, for little-correlated features, more p.c. will be needed (Phan, 2016).
26
Results
Sociological aspects
Figure 14: Networks’ size for the 9 farms belonging to Paniere Flegreo. The total size is indicated by the blue colour. Instead, different colours represent the sectorial size for each typology, for each farm (Yellow=New relations; Grey=Knowledge improver; Orange=Material-Economic)
The network size, reported in the graph above (Fig. 14), has been calculated as a measure to frame the companies’ social net. It was obtained counting the ties indicated within the questionnaires, then reported on Gephi. The companies show a different rate of ties depending on their typology. Il Giardino dell’orco (G.O.) shows the biggest total size and the biggest sectorial sizes too. This could be related to the fact that this farm has a prevalent social dimension. Comparing it with the others, G.O. exploits very much its social dimensions organising constantly events in its open spaces. These events are the most disparate, going from culinary meetings to yoga sessions. The other farms seems to have quite homogenous sizes, the majority of them with at least a dimension of four total ties, excluding Km 0 Flegreo and Az. Agricola Costagliola. This depict all of them as little family business, which indeed is coherent with the need of having a collective organism as Paniere Flegreo can be. Considering the three relational typologies: material- economic, knowledge improver, new relations, the one that appears more developed is “new relations” that indicate relations that didn’t start motivated by technical improvements (knowledge improver) or because of labour relations (material-economic). This is a clear indicator of how the spontaneous social dimension is still important in family-run farms.
27
Figure 15: Multiplexity calculated for the 9 farms belonging to Paniere Flegreo. Three colours, Red, Yellow and Green have been used to indicate a different relational levels. Respectively: one-type relation, two-types relations, three-types-relations
The diagram here presented (Fig. 15) depicts the multiplexity for each farm in a pie-chart form. This chart offers the possibility to examine the percentage of ties having one, two or three relational types, while comparing the farms together. The maximum level is set up at three and the minimum is set up at one. It is important to remark that this indicator doesn’t distinguish between the relation typologies, namely material- economic, knowledge improver, new relations, but merely counts how many of them are verified together. The result is interesting to quickly evaluate the strength and the diversity of the farms relations. Indeed, it is thought that the strength of a tie between two actors depends on the number of topics and motivations they have in common (Paglione, 2010) more than its old-age. It is also proven that not only having many relations, but even more showing diverse relations benefit to companies (Marsden, 2012). Il Giardino dell’orco appears to have well mixed ties’ multiplexity, which is coherent with its network size. The farms that show a prevalence of 1-type relations are Km0 Flegreo and la Sibilla. Az.Agricola Costagliola, instead, show the particular condition of having a totality of 2-types relations, that is, however, a better condition than having many 1-type ties. Personally, I would have expected to find out more 3-types ties within Paniere Flegreo’s companies, because the organization itself is already a platform that promotes friendship and trust between its participants. Probably, some aspects of its functioning should be revised to guarantee a long-lasting lifetime.
Having a general picture of farms network dimension and multiplexity, allows to proceed exploring the singular relations in every network. Indeed, the maps obtained with Gephi are following. Paniere Flegreo is always indicated in the maps with the acronym “PF”. The other names are simply abbreviated. Their identification is not needed for the analysis purposes. 3In fact, performing the ego-centred SNA, has
permitted to pay only little attention to the actors in connection with the farms, while letting the full focus on the farms. The maps are represented in two colours: pinkish tones are associated with multiplexity and brownish tones refigure gratuitousness’ motivation. Darker colour are indicating higher values of these parameters. Reciprocal ties are deducible from both the graphs typologies, thanks to the double arrows, indicating both in-going and out-going directions. When the arrows are darker than their lines, this suggests that a parameter (motivation or multiplexity) is higher in one sense of direction (e.g.: ingoing or outgoing). After that, the overall number of ties showing relational good nature per farm (Table 10) is presented in an aggregated chart.
3 Before starting the interview, the respondents were asked to sign the privacy consent module. Third parties should not feel involved, because the questionnaires can only report the respondents feelings. This do not necessarily represent the reality.
28
Figure 16: Multiplexity colour code map of Il Giardino dell’orco
29 As noticed before, G.O. has surely the most numerous ties. All of them are reciprocal and none of them shows a motivation under the score of 8. Therefore, G.O. is also the farm with more connections showing the relational goods characteristics. It must be said, however, than none of these ties has a very high score (maximum= 16,16).
Table 4: Relational goods identification in Giardino dell’Orco network. Right column: actors; left column: ingoing and outgoing scores.
Restaurant FdC 11,11
Restaurant Lab2 11,11 Elementary school 11,11 Orto Urbano group 12,12 Ass. Agrigiochiamo 11,11 Stufe di Nerone 10,10 Paniere Flegreo 11,11
Parco Cerillo 11,11
Corto Circuito Flegreo 11,11
Cantine Averno 11,11
Neighbors 11,11
Schools FarmProject 12,12
30
Figure 19: Gratuitousness’ motivation colour code map of Masseria Sardo.
Masseria Sardo reaches the number of four ties. All of them seem very strong in motivation. Out of four relations, only one (Direct Customers) is not reciprocal. The remaining three are reciprocal and attested at the highest motivational scores of this study. Thus, Masseria Sardo presents three and strongly rooted connections showing the relational goods features. Considering that it is a young farm, only one year old, its start bolds well.
Table 5: Relational goods identification in Masseria Sardo network. Right column: actors; left column: ingoing and outgoing scores
CAC 14,13
Paniere Flegreo 12,12
31
Figure 20: Multiplexity colour code map of Bobobio
Figure 21: Gratuitousness’ motivation colour code map of Bobobio
Bobobio seems to have strong ties too. All of them are well motivated, but only three out of five are reciprocal. Therefore, Bobobio has 3 relationships showing the properties of relational goods. It is interesting to notice that Bobobio shows the higher value of multiplexity, so many shared purposes, with Paniere Flegreo.
32
Table 6: Relational goods identification in Bobobio network. Right column: actors; left column: ingoing and outgoing scores.
Ass. SMF 11,12
Paniere Flegreo 10,12
Gas_M 14,12
Figure 22: Multiplexity colour code map of la Sibilla
33 La Sibilla does not show any reciprocal tie. Thereby, no relational goods could have been identified. However, both the motivation and the multiplexity appears to have decent levels for many relational ties, especially in the cases of Proposta vini and Direct Consumers. Hence, the absence of reciprocated relations appears unexplained. Knowing personally the situation, I should probably say that in the first phases of aggregation, when Paniere Flegreo was just emerging, this company contributed a lot in motivating and leading together the others. Eventually, they noticed that their efforts were not counterbalanced. This led to a progressive closure towards the others farms. This kind of dynamic should be prevented and avoided in little communities, because their real strength lies in their unity and cohesiveness.
Figure 24: Multiplexity colour code map of Salemme
34 Salemme demonstrates in general an high level of multiplexity and a discrete motivational level. Nonetheless, the only reciprocated relation, consequently the only one showing the relational good nature, is the one with Paniere Flegreo.
It is relevant to notice that in some farms, namely Masseria Sardo, la Sibilla, Salemme and Km0 Flegreo, the relation with “Direct Consumers” is strongly motivated. This indicates the affection of the bounds that are created when clients come assiduously to the farm, to personally choose products and to talk to the producers, privileging the human and social dimension over the pure transaction.
Table 7: Relational goods identification in Salemme network. Right column: actors; left column: ingoing and outgoing scores.
Paniere Flegreo 14,9
Figure 26: Multiplexity colour code map of Km0 Flegreo
35 Km0 Flegreo has a tiny network, below the mean size of the other farms belonging to Paniere Flegreo. The tie developed with Paniere Flegreo satisfies the reciprocity and motivation requirements, thus can be identified as a relation good. However, looking at Figure 26, where the multiplexity is investigated, it is possible to notice that the arrow from PF pointing to Km0 is darker than its line. This indicates that the in-going level of multiplexity (from PF towards Km0) is higher than the outin-going.
Table 8: Relational goods identification in Km0 Flegreo network. Right column: actors; left column: ingoing and outgoing scores.
Paniere Flegreo 10,10
36
Figure 29: Gratuitousness’ motivation colour code map of Contrada Salandra
While Contrada Salandra has high level of multiplexity and motivation in many of its relations, it does not show any reciprocal tie. Looking careful at the arrows, it can be seen that many of them point towards the outgoing direction (from Contrada Salandra to others). This reflects the modus operandi of this farm. Indeed, the couple -husband and wife- that runs this company since many years, are used to be lavish in helping friends and colleagues, but at the same time they are also closed and self-sufficient when it comes to their own activity. Without taking anything form the quality of their work, which produces a tasteful and organic wine, their quality life would be probably improved if letting other colleagues from Paniere Flegreo group help them. In this case, a more proactive contribution from Paniere Flegreo’s companies, could be successful.
37
Figure 31: Gratuitousness’ motivation colour code map of Costagliola
Costagliola has a little network, composed only by three relations. However, all of them have high multiplexity level, meaning that many interests are shared between the actors engaged. All of them satisfies the requirements in terms of reciprocity and motivation, in order to be labelled as relational goods. This representation seems to me coherent with reality. Indeed, the family that runs the activity from two generations is very propense to trust and contribute to community dimensions. Consequently, the three relations that they have indicated are developed with associations.
Table 9: Relational goods identification in Costagliola network. Right column: actors; left column: ingoing and outgoing scores.
Paniere flegreo 11,11
Campagna Amica Coldiretti 12,11 Ass. Pomodoro Cannellino 12,11
38
Figure 32: Multiplexity colour code map of Paesano
Figure 33: Gratuitousness’ motivation colour code map of Paesano
Paesano shows a picture resembling the ones of Sibilla and Contrada Salandra. Both multiplexity and motivations seem developed in almost every relation, but none of them is reciprocated. In addition, excluding the tie with Paniere Flegreo, Paesano is only connected with the owners’ relatives, which sometimes decide to help in the farm with some labour. In this way, Paesano appears to be the most isolated farm in the Paniere flegreo group. No relational goods have been detected for this farm, as for la
39 Sibilla and Contrada Salandra. Are there other similarities that bring in common this companies? All of them are between the oldest that joined Paniere Flegreo, all of them have shown to possess high biological soil quality (slightly over the others’ average). In my opinion these companies have invested a lot in the first aggregation phases, but then when Paniere Flegreo did not grow accordingly to their expectations, they stopped to believe and, in some case, to contribute in the community. This has meant a first disintegration phase, which should be absolutely fixed to preserve the community.
Table 10: Number of ties showing relational good nature per company. Identification criteria: reciprocity and gratuitousness motivation score higher than 8.
Farm N° of RG Giardino dell’Orco 12 Masseria Sardo 3 Bobobio 3 Costagliola 3 Salemme 1 Km 0 Flegreo 1 Contrada Salandra 0 Sibilla 0 Paesano 0 Average 2,5
In conslusion, Table 10 summarizes the relational goods identified per company, in descending order. The contrast emerges evident between the oldest farms (Contrada Salandra, Sibilla, Paesano) and the youngest (Giardino dell’Orco, Masseria Sardo, Bobobio, Costagliola). G.O. is clearly an outlier, moreover the farm is not young in age since its establishment, but the actual administrative farmer is the legal owner’s son, who is applying a modern governance. The same thing is valid for the Costagliola farm too, which now is run by the second generation. Bobobio and Masseria Sardo are companies old just 1 year and 1 year and an half. For what concerns Salemme and Km0 Flegreo farms, showing only 1 tie each, responding to relational goods criteria, they are- respectively- 2 and an half years old and 4 years old.
Apparently, the distinction trait has been found between farms with a young governance, able to establish relationships associated to relational goods and old-style farms, closer to an older model of agricultural business, less suited to social dimension at the present time.
40
Ecological aspects
Figure 34: QBS-ar (green) and QBS-c (blue) average values for the 9 farms belonging to Paniere Flegreo
The above graph (Fig. 34) presents QBS-ar and QBS-c averages for each farm. The company are shown, from the right to the left, in the exact chronological order in which they were sampled. This allows the reader to quickly realise how and if the progressive temperature warming has impacted the results. As explained in the methodologies, the whole sampling operation was carried out in less than two months. However, the sudden changes of wheatear which were registered this spring in Campania could have altered the results. Nevertheless, it is reassuring noticing that, even if the first two companies (Giardino dell’orco and Masseria Sardo) show some of the lower values, the third one (Bobobio), sampled less than ten days after the first one, reports instead the highest values.
In general, it is possible to affirm that the QBS indexes have shown results in line with the publications of the last two decades about soil quality in cultivated lands. Indeed, the values registered are coherent with the ones registered from ARPAV in cultivated areas from 2012 to 2015 ( “Monitoraggio della qualità biologica del suolo nel veneto: 2012-2015”) and to the ones reported from ERM (https://www.erm.com/) in 2017, in the study of cultivated soil in Melendugno, Lecce, Puglia in “Report Monitoraggio Ante Operam del Suolo Determinazione QBS-ar”. If comparing the recent study “Microarthropods biodiversity in natural, seminatural and cultivated soil ---QBS-ar approach” (Menta et al, 2018), that sampled three different typologies of soil: agricultural, alfa-alfa, woodland, the results here reported appear closer to alfa-alfa average scores, than agricultural average scores. Bobobio and Contrada Salandra results appear even more close to woodland average scores. Unfortunately, not official studies using QBS indexes have been carried out in Campania yet, so a more specific comparison is missing. However, this study could represent a first approach to promote the QBS method in Campania too.
In summary, the majority of the values reported in this section are on the average for cultivated lands, while Bobobio and Contrada Salandra stand closer to values registered in biological regime, confirming their tendency to go organic.
Finally, the main advantage of this diagram is to synthetize the numerous results I had and make them readable, but its main bias is probably the loss of accuracy necessaire to describe not only in general terms the soil quality in each farm, but also its quality diversity between different sites within the same farm.
41 However, to accomplish even deeper understandings, the integral diagram with all the sites’ result is reported in the Appendix (Fig. 3).
Figure 35: Mites and Collembola mean densities for the 9 farms belonging to Paniere Flegreo
42 The Mites/Collembola indicator (or Acari/Collembola ratio) is an environmental stability indicator (Bachelier, 1978), it helps detecting the level of equilibrium between pedofauna’s communities. Normally, values higher than one are considered proper of good natural condition, while values lower than one can indicate soil degradation. The graph just above reports the ratios calculated on mean densities per farms (Fig. 36). The A/C mean densities are reported in the previous chart (Fig. 35). Only Az. Agricola Paesano shows a rate lower than one, indeed this farm rises on an ex tuff quarry expropriated by the Camorra around ten years ago. It is possible that the pedofauna is still into a regeneration process. However, when looking at the two mean values reported in the first graph (Fig. 35), Paesano shows quite normal values for Acari and Collembola, respectively 20,75 and 26,75 individuals average. This allows to re-interpret the situation and to lighten its negative meaning, indicating that the restoration process is going on well. Masseria Sardo presents also a border line A/C value. Moreover, this farm has very low values in the mean densities too (Fig. 35). In its case, these values can be attributed to the recent installation of the company on its lands. Indeed, they only started their activity since less than a year, recovering lands used for traditional agriculture with heavy use of pesticides. On the contrary, Az. Agricola Costagliola show a particularly high A/C value. Also in this case the value has to be reconsidered looking at both graphs. Indeed this value is generated mainly from the disequilibrium between mites mean density (40,4) and collembola density (2,6). In general, the remaining farm show normal or good values for cultivated lands. Also for mites and collembola densities, the integral diagram, reporting all the values for each site within each farm is reported in the Appendix (Fig. 3).
43
PCA results. Linking social and ecological aspects
Figure 37: Data reordering using PCA. First principal components on the x-axis: QBS-ar and QBS-c. On the y-axis, the other principal components selected: sectorial network categories and multiplexity levels
The PCA analysis offered the possibility to re-arrange the data, observing them from a new point of view and easing a comprehensive view of the ecological and sociological aspects together. In this case the multidimensional reordering (expressed by the x-axis) has been performed using the QBS-ar and QBS-c results. Contemporarily, the y-axis is superimposing the chosen sociological indicators: the sectorial network size per category (new relations, material-economic, knowledge improver) and the multiplexity levels(1-type, 2-types, 3-types relations).
To read properly this chart, it is necessaire to take in mind that on the left side are represented the farms with higher biological soil quality, while on the right side the farms with the lower biological soil quality. Instead, the directions indicated by the blue lines determine the positioning due the social indicators results. Indeed, the companies that are showing a considerable rate of 3-types relations are: Il Giardino dell’orco, Masseria Sardo and Bobobio. Therefore, the three of them are in the upper part of the graph, following the direction given by the blue line indicating 3. Bobobio is moved to the left, because its biological soil quality is the highest. On the other hand, Costagliola who is characterized by the totality of 2-types relations is collocated towards the bottom, following the direction of the blue line labelled 2. With this way of reasoning, it is possible to interpret the graph dividing the nine companies in three main groups:
1) Bobobio, Contrada Salandra.
This group is characterized for very high biological soil quality, while the networks are prevalently constituted by new relations and material-economic ties category.
44 2) Sibilla, Paesano, Salemme, Costagliola, Km0 Flegreo.
This group is the most numerous, collecting five farms out of nine. These farms are characterized by a medium level of soil biological quality and by networks tending to have a medium-low (1-type, 2-types ties) level of multiplexity.
3) Masseria Sardo, Giardino dell’Orco
The third group is discernible for the low biological soil quality, but also for the high developed multiplexity level, demonstrated by an high percentage of 3-types ties.
The first and the third group, which have respectively the highest and the lowest biological soil quality, do not associate a clear social profile to their ecological one. Indeed, both of them are collocated in the higher part of the chart, making unrealistic to find a distinctive social trait that could influence the ecological condition, or vice versa. Instead, the second group presents a coherent behaviour between ecological and sociological level. The companies belonging to the second group seem to stagnate into an ordinary profile. At the end, this research was not able to identify a social behaviour that strongly determine an ecological response, neither an ecological state which is able to determine a social approach. However, for the majority of these farms, the mediocrity seems to be, surprisingly, the only real “trait d’union” between the social and ecological aspects examined within this research. Recalling the history of Campi Flegrei, the mediocrity could have represented, during the hard years, a strategy to survive, but today, in the green revolution years, it needs to be outdated. It is the task of future researches, to enlighten the right path in order to guarantee the social and ecological blossom of the countryside.
45
Discussion
The aim of this study was to explore and evaluate the ecological soil quality in 9 farms joining the local organization Paniere Flegreo in Campi Flegrei (NA), Campania, Italy. At the same time, it evaluated social relations between companies and other institutions, competitors or clients, trying to find out if and how the social environment mirror in the ecological environment.
Following the just mentioned track, this research tried to answer some questions:
Are the Soil Biological Quality Indexes, used to analyse soil quality in cultivated Mediterranean areas, showing results in line with the publications investigating Italian cultivated soil quality in the last two decades?
Does the Social Network Analysis enable to detect relational goods between farms and different actors? Does the social environment impact in any ways the ecological environment (and/or vice versa) of the farms joining the local organization Paniere Flegreo?
The soil biological indexes used, namely: QBS-ar, QBS-c and Acari/Collembola ratio, confirmed their applicability in Mediterranean cultivated areas. The collection, the extraction-lab installation, the monitoring and the analysis were easy, un-expensive and practical. Moreover, the QBS indicators do not require the specimens count, because they are based on presences, without considering densities. They do not even require the identification at the species level, making possible to use morphological keys at higher taxonomic level. This makes these indexes feasible and exploitable from any common operator without requiring specific knowledge, thus eligible to be widespread and adopted as common and comparable indicators within the scientific community. The results showed to be in line with the examined publications on Italian biological soil quality performed in the last twenty years (Gardi et al 2006, ARPAV 2012-2015, ERM 2017, Menta et al 2018). As highlighted in the Result section, the scores collected for ar and QBS-c indexes are attested around the average values, or in some farms QBS-clearly above it, for agriQBS-cultural lands. In general, high QBS-ar were accompanied by high QBS-c and vice versa, confirming, the one with the other, a certain biological activity. For what concerns the Acari/Collembola ratio, comparing it with recent results collected on Italian agricultural soil has been difficult: this index is more often used in woodlands, uncultivated lands or remediation sites. However, its reliability was assessed by the capacity to detect soil histories (as for Paesano, with the expropriated tuff quarry, or for the just established Masseria Sardo), providing explanations with tangible quantification in numbers and in terms of soil biological qualities. The Social Network Analysis has represented a useful and intuitive method to detect relational goods between farms and different actors. The questionnaire should be probably improved for future similar studies. Indeed, making it understandable for farmers unused to sociological analysis, was one of the main complex step. This is why, I have preferred to not set up a fixed time to complete it, but let the respondents have all the time they required. Also the different coding for different questions could have mislead their answers. Instead the mapping with Gephi was one of the most simple step, helpful for the visual understanding, which characterizes the SNA method. For future analysis I propose to make the respondents draw their own relational net and then complete the questionnaire. In fact, I believe that the advantages of the visual analysis must be shared not only between examiners and readers, but also expanded to the interviewed. This would help avoiding biases and making the whole interview process more enjoyable. However, even if the data gathering phases should be rearranged to fit better the agricultural multidimensionality, overall the analysis has demonstrated to be proper in detecting relational goods, which are a complex and particular concept to capture.
