The social networks of Irish private forest owners: An exploratory study

Forest Policy and Economics xxx (xxxx) xxx–xxx

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The social networks of Irish private forest owners: An exploratory study Evelyn M. Stoettner⁎, Áine Ní Dhubháin School of Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland

A R T I C L E I N F O

A B S T R A C T

Keywords: Social network analysis Ego-centric networks Decision-making Information exchange Knowledge transfer Harvesting behaviour

Private forests in Europe are increasingly characterised by fragmented ownership and declining owner knowledge and engagement, raising concerns about the harvesting behaviour of private forest owners. The formation of forest owner groups is seen as a means not only of addressing fragmented ownership but also owner knowledge. It is also increasingly recognised that owner engagement is not only influenced by the characteristics of the owner and their forest but also the individuals that surround the owners, i.e. their social network. This study aims to explore and describe existing social networks of private forest owners in Ireland. It focusses specifically on four types of owners: forest owners who were members of a forest owner group and who had harvested; forest owners who were members of a group but had not harvested; forest owners who were not members of a group and who had harvested and finally forest owners who were not group members who had not harvested. Interviews were held with a total of 56 forest owners in the southern half of Ireland. Members of forest owner groups who have harvested had the largest and the most diverse social networks. These results suggest an association between social networks and the harvesting activity of forest owners, although the direction of the association is not clear. The persons/organisations in the social networks that were trusted most by the forest owners were the public technical advisory service (Teagasc), the forest owner group and family/ friends/neighbours. Teagasc and the forest owner group were also the most influential highlighting the key role that trust plays in knowledge exchange. The study provides the first insights into the social networks of forest owners in Ireland. However, further research is required to address how social networks effectively influence forest owners' harvesting behaviour.

1. Introduction Demand for wood for forest industries (Mantau et al., 2010) and for energy (Blennow et al., 2014) in Europe is expected to increase. With half of the forest land in Europe privately owned the actions of private forest owners, in particular their harvesting behaviour, will play a key role in determining the extent to which this increased demand can be satisfied by wood supplied from European forests. Whether private forest owners harvest or not is influenced by a range of factors (Beach et al., 2005). From a policy perspective, the provision and uptake of technical assistance or cost-sharing programmes increases the likelihood that an owner will harvest (e.g. Amacher et al., 2003; Silver et al., 2015). Larger forest plots are more likely to be harvested (e.g. Kuuluvainen et al., 1996; Hendee and Flint, 2013; Silver et al., 2015), due to the economies of scale associated with them. The accessibility of the forest site is also important, with harvesting more likely to occur on sites with road access (e.g. Conway et al., 2000; Levers et al., 2014). The objectives of forest owners also influence their harvesting behaviour (Silver et al., 2015). Owners who

⁎

value recreational opportunities most strongly or who do not have any particular objectives for their forest harvest less frequently and harvest smaller volumes than multi-objective and self-employed owners (Favada et al., 2009). Owners' forestry knowledge (or lack of) also plays an important role with Megalos (2000), cited in Wicker (2002), noting that forest owners identified their lack of forestry knowledge as a significant deterrent to them managing their stands for timber production. In Europe, the character of private forests and their owners is changing. Fragmentation of forests is increasing due to inheritance and restitution (Schmithüsen and Hirsch, 2010). There are increasing numbers of “new” forest owners; these are typically non-farmers and/or urban dwellers who frequently do not reside close to their forest (Karppinen, 1998). Associated with these demographic changes in ownership are changes in values and objectives for forest ownership. The proportion of owners who are economically dependent on their forests is decreasing (e.g. Kvarda, 2004) while the proportion holding multiple objectives (including amenity and nature conservation objectives) is increasing (Ní Dhubháin, 2011). The increasing fragmentation of private forests and increasing

Corresponding author. E-mail addresses: [email protected] (E.M. Stoettner), [email protected] (Á. Ní Dhubháin).

http://dx.doi.org/10.1016/j.forpol.2017.09.008 Received 6 March 2017; Received in revised form 4 July 2017; Accepted 14 September 2017 1389-9341/ © 2017 Elsevier B.V. All rights reserved.

Please cite this article as: Stoettner, E.M., Forest Policy and Economics (2017), http://dx.doi.org/10.1016/j.forpol.2017.09.008

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forest owners who were not group members who had not harvested. The second aim goes beyond pure social network features (such as network size and composition) and aims to include an understanding of social capital features also. Hence the second research question is: How do the perceptions of how influential and trustworthy the actors in the network are differ between the four types of owners? Ireland is a useful empirical case in which to undertake this research for a number of reasons. First, similar to the situation that prevails elsewhere in Europe Ireland is also witnessing the emergence of “new” forest owners. However, in contrast to their European counterparts, the forests these new owners own are also new. This situation has arisen because traditionally afforestation was undertaken by the State and it is only in the past 30 or so years that private landowners have opted to afforest, primarily in response to generous financial incentives from the EU and latterly the Irish Government (DAFM, 2015, p. 10). Hence almost all forest owners in Ireland lack traditional knowledge in forest management. Second, the harvesting behaviour of these owners is attracting attention as the domestic demand for wood is expected to exceed supply in Ireland and this shortfall is expected to be met by increased wood harvesting in private forests (COFORD, 2015). Yet, there are concerns that the timber supply from private forests will not be realised with the latest National Forest Inventory in Ireland identifying that > 20% of private forests established since 1980 have not been thinned although they are mature enough (Forest Service, 2013, p. 124). Third, the formation of forest owner groups is also being encouraged in Ireland. Since the first group was established 10 years ago, a further 20 groups have been set up with government and Teagasc1 support (Teagasc, 2012). The further expansion of forest owner groups in Ireland is being encouraged, particularly in the context of knowledge transfer (COFORD, 2015). Thus, Ireland is facing similar challenges to the rest of Europe with regard to wood supply and private forest owners, with the issue of owner knowledge arguably more acute than it is elsewhere in Europe. Hence, the results of this study will not just apply to Ireland but will also have relevance for the other European countries where the numbers of owners with no traditional forestry knowledge are increasing.

diversity of objectives among forest owners raise questions as to what products and services, in particular timber, will be delivered from private forests in the future. The establishment of forest owner groups is being promoted as a means of overcoming some of the challenges of fragmentation and such groups are considered to be an effective way to increase wood mobilisation (e.g. Schlüter, 2007). The increasing diversity in forest owners' objectives has highlighted the need for forest advisory bodies to adapt the services they deliver and to change the service delivery mode (Korhonen et al., 2012). However, what has attracted less attention is that the structural changes in ownership will mean that increasing numbers of “new” forest owners will lack the traditional knowledge of forest management that their predecessors had. As knowledge of forestry is a significant factor in the decision to harvest, finding a means of addressing this knowledge deficit among new owners will be important. Forest owner groups have a potential role to play in this regard as they offer a forum for forest owners (new or otherwise) to interact with and learn from their peers. The increasing emphasis on forest owner groups as a potential solution to some of the challenges facing harvesting in private forests presents an opportunity to investigate the decision-making of forest owners through a network lens. To date studies on the behaviour of forest owners have tended to focus on how individual characteristics of the owners and their forests influence owner behaviour; the role that the social network of an owner might play has received less attention. A social network describes the relationship between individuals or organisations, and the significance and roles of these relationships (Wasserman and Faust, 1994). Two levels of social networks can be defined: (i) An ego-centred network is specific to one individual and pictures what connections this particular individual has to other individuals (Knoke and Kuklinski, 1982, p. 16). (ii) A complete network is covered when all connections between all individuals within a defined group are identified; this group can be of any size, e.g. a small school class or a national industry (ibid., p. 17f). The connections or ties between those in a network can be described as existing (or not, i.e. have a binary value) or can be elaborated upon to include the frequency or the nature (e.g. level of trust) of the tie(s) (Jackson, 2010). Social network analysis aims to uncover interactions among persons or organisations and usually addresses: a) the individual's characteristics, and b) the connectivity and interactions of the individual with other individuals (Coleman, 1994). Of particular interest in network analysis is how interactions with other individuals change an individual's characteristics or vice versa (ibid.). Social network analysis has been used in forestry-related studies in Europe and in the US, yet there has been limited focus on the specific connection between forest owners' social networks and land management practices (Sagor and Becker, 2014). However, those studies that do focus on such a connection (e.g. Schraml, 2003; Knoot and Rickenbach, 2011; Kittredge et al., 2013; Kueper et al., 2013; Ruseva et al., 2014) report that social networks do extend an individual's knowledge and can increase adoption of new behaviour. In a wider context it has also been shown that social networks help build social capital as they provide access to resources (e.g. knowledge) that are less accessible when the individuals are not connected to others (Bourdieu and Wacquant, 1992, p. 119). The aim of this paper is to explore and describe existing social networks of private forest owners; focussing specifically on how these may vary according to membership of owner groups and previous harvesting behaviour of the owner and what role they play in knowledge transfer; the combination of these three issues has not been the focus of previous studies. Thus the first research question addressed in the study is: How do social networks differ regarding network size, composition, and diversity according to whether an owner is a member of a forest owner group (or not) and whether he/she has harvested (or not), thus four types of owners were compared: forest owners who were members of a forest owner group and who had harvested; forest owners who were members of a group but had not harvested; forest owners who were not members of a group and who had harvested and finally

2. Methodology 2.1. Study area The study area is the south-eastern NUTS 2 administrative region of Ireland, comprising 43% of the total area of the country. The total forest area in the study area is 395,060 ha, which represents 10.9% of the region's total area. More than 48% of the study area's forests is in private ownership, most of these forests are younger than 20 years. The area is one of the 14 model regions of the SIMWOOD2 project and it was chosen to be so as it comprised a number of the most active forest owner groups in Ireland. Two of these groups participated in this study, namely the Irish Wood Producers and Limerick and Tipperary Woodland Owners. Both groups finance their services with an annual membership fee and with support from other bodies such as LEADER3 and Local Authorities (Teagasc, 2012). 2.1.1. Irish Wood Producers Ltd. (IWP) IWP, founded in 2010, has almost 650 members with just over 7000 ha of forest in five counties. The staff of the group is comprised of 1 Teagasc is the Agriculture and Food Development Authority and provides independent advice to farmers including forest owners; also organises field days and produces a newsletter for forest owners. 2 This study is conducted as part of the SIMWOOD project which is funded by the European Commission's 7th Framework Programme. The aim of the project is to increase sustainable mobilisation of forests in Europe by addressing technical and social barriers to it (see SIMWOOD, 2016). 3 An initiative for rural development projects, supported by the European Union.

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influence assessment the owners were provided with equal sized wooden pieces to create ‘influence towers’ as per Schiffer (2007). They were asked to stack these wooden pieces on top of the name of each person/organisation they had previously listed on the piece of paper. They were free to use as many pieces as they liked but were advised that the number of pieces, i.e. the height of the influence tower, should be an indicator of the strength of the influence; the higher the tower, the stronger the influence. The forest owners were also asked to identify those within the network whom they trusted and to elaborate on why they trusted these persons/organisations. The degree to which people in a network mix with those who are like them is a feature of networks that is often investigated in social network analysis (Newman, 2003). In this study this feature was investigated by classifying every member in the network by their “interest” in forestry. To do this, the forest owners were first presented with five statements and were asked to choose which of the five described their interests in forestry (owners could choose more than one). The statements used were based on those that were drawn from indepth interviews with Irish farmers by Duesberg et al. (2013); and used thereafter by Duesberg et al. (2014) in a nationwide study to determine farmers' motivations for afforesting land. The statements cover the spectrum of reasons why landowners choose to afforest land and could be broadly classified as profit reasons (three statements); environmental protection reasons, and development of forestry (as an industry) reasons. During the interviews, forest owners were also asked to indicate what they perceived to be the interests in forestry of the persons/ organisations they were in contact with; i.e. they assigned one or more of the following three labels to the persons in the network: “profit”, “environmental protection”, “development of forestry (as an industry)”. During the interviews forest owners were asked about their harvesting decision. First they were asked whether they had engaged in harvesting to date. They were then asked to identify the persons/organisations who were important to them in the harvesting process (i.e. from decision-making to marketing). The lengths of interviews ranged from 23 min to 2.5 h with an average of 63 min. All except one forest owner gave permission to record the interviews, which were then transcribed.

three forestry professionals, who work with forest owners and harvesting contractors as well as timber buyers, and one officer responsible for overall administration. IWP also has a board of ten elected directors. The aim of the group is to enhance the value of private forest land by providing training and information services to their members on best practice in forest management. A further aim is to facilitate the formation of clusters of forest owners for joint harvesting and better access to local markets. The services that IWP provides include the completion of applications for subsidies, site assessment, and planting; its primary service is to organise the harvesting and marketing of timber (Teagasc, 2012; IWP, 2016). 2.1.2. Limerick and Tipperary Woodland Owners Ltd. (LTWO) LTWO was established in 2010 in order to pool the resources of private forest owners in two counties. The group has over 80 members who own, in total, > 2000 ha of forest land and is comprised of 10 clusters of forest owners. LTWO has an elected committee and is supported by Teagasc advisors. Meetings and field days are organised for members to help them develop good forest management practice. The aim of forming the clusters and providing information is to contribute to the objective of maximising the financial returns to the forest owners. Services that LTWO provides are, among others, completion of applications for subsidies, site assessment, planning and supervision of harvesting, and timber marketing (Teagasc, 2012; LTWO, 2016). 2.2. Study participants Names and addresses of forest owners are not publicly available in Ireland. Hence to access a sample of owners the co-operation of the two forest owner groups and one Irish forestry company dealing with forest owners in the study area was requested. Representatives from these three organisations agreed to stratify their client list of forest owners into those who had harvested and those who had not (their membership/client lists were also not available to the authors). This yielded four strata/types from which to choose the sample from: members of a forest owner group who had harvested, members who had not, nonmembers of owner groups who had harvested, non-members who had not harvested. Financial and time constraints limited the total sample size to approximately 60 forest owners. Forest owners in each of the four strata were invited, in random order, to participate in the study. The final sample was comprised 56 forest owners, 33 were members of a forest owner group (where 24 had engaged in harvesting and 9 had not) and 23 were non-members (where 16 had engaged in harvesting and 7 had not).

2.3.2. Data analysis The size of an individual owner's network was calculated as the number of forestry-related persons/organisations the forest owner was in contact with. The composition of the network was described by assigning every individual person/organisation identified by the owners in the interviews to categories (Table 1). The diversity of the network measures to what extent the number of categories of persons/organisations that compose an individual owner's network differ. It was calculated in two ways in this study. First, a simple count of how many different categories appear in each owner's network was made. A second assessment of diversity was made using a standardised version of Blau's Index also referred to as IQV (Index of Qualitative Variation) (cf. Knoke and Yang, 2008, p. 55) where: IQVi = [1 − ∑ pj2]/[(k − 1)/k], i is ego5 (i = 1, …, 56), p is the number of connections that an ego has with a category of alter6j (j = 1, …, 5 to 77), expressed as a proportion of the total number of connections an ego has, and k is the number of maximum categories (k = 5 for non-members; k = 7 for members). The IQV ranges from 0 to 1, where 1 means a perfect diversity of categories within a social network. The strength of the influence of every person/organisation in the network on each forest owners' forest-related decisions was assessed

2.3. Methods 2.3.1. Data collection Each one of the 56 forest owners was interviewed. During these interviews forest owners' ego-centric social networks were identified using the tool Net-Map developed by Schiffer (2007) that can be used to transform the implicit knowledge (cf. Polanyi, 1985) about a person's social network into an explicit expression. Using Net-Map, the forest owners4 first wrote down all persons or organisations they were in contact with in the context of their forests on a blank piece of paper. They were then prompted with the following open-ended questions about the connections they had to those persons: Who do you talk with about forestry in general? Who do you go to when you have a specific question about your forest? Who supports you with material (i.e. equipment or labour)? The owners were asked who among the persons/organisations they identified influenced them in their forest-related decisions. They were also asked to indicate the strength of this influence; to support this 4

5 In ego-centric networks, ego is a specific individual in the centre of the network who is connected to others (Hanneman and Riddle, 2005). 6 In ego-centric networks, alters are all individuals that are connected to ego (Hanneman and Riddle, 2005). 7 The categories “Staff” and “Members” only apply to members of a forest owner group.

Ideally; if the forest owner was unwilling to do so, the interviewer took that role.

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Table 1 Description of the categories of persons or organisations in the social networks of participants. Category

Description

Forest service Teagasc

Public inspectors and officers of the State's Forest Service, Department of Agriculture, Food and the Marine The Agriculture and Food Development Authority – provides independent advice to farmers including forest owners; also organises field days and produces a newsletter for forest owners Private persons or organisations who offer trained forestry-related information or forest management work to private forest owners; e.g. Private forestry consultant, forestry company, representative bodies for timber growers Private or public persons or organisations who work specifically for harvesting or marketing; e.g. Harvesting company, lorry driver, sawmill Private persons; e.g. Other forest owners, family, friends, neighbours Staff of the forest owner group; e.g. Forester, manager, officer Other members of the forest owner group

Private consultant/manager Logger Family/friends Staff Member

small sample sizes within the four owner types the non-parametric Kruskal-Wallis test (Kruskal and Wallis, 1952) was used to test for differences between the four types of owners. Where differences between owner types were shown to be significant, post-hoc pairwise comparisons were made using Dunn's test for non-parametric data (Dunn, 1964). In the section of the results relating to trust, examples of the reasons given by the forest owners as to why certain persons/organisations are trusted are given in the form of direct quotes.

using the influence towers built by the owners. The number of wooden pieces that each forest owner had allocated to each person/organisation was counted and those numbers expressed as a proportion of the number of pieces in the tallest tower that each owner had created. The interests (i.e. profit, environmental protection, development of forestry) of the forest owner and of the other persons in the network were compared to provide an indication of the extent of assortative mixing where this describes the preference of a person to connect to others that are similar (Dorogovtsev and Mendes, 2004). As forest owners and others in the network could hold more than one interest, assortative mixing was assessed separately for each interest. To do this, the numbers of connections between forest owners and alters were presented in a matrix according to whether the owners and alters held that particular interest (or not). Table 2a shows an example of an assortativity matrix for the profit interest. An assortativity coefficient was ∑ e −∑ a b calculated as follows: r = i ii i i i , where ai = ∑j eij and bj = ∑i eij

3. Results 3.1. Network size The social networks of the forest owners, in relation to forestry issues in general comprised, on average, 7.4 persons/organisations, ranging from 2 to 20 (n = 56). Those persons/organisations were identified primarily as sources of information for the forest owner (i.e. 74.2% of all connections were based on information exchange); persons/organisations providing material (e.g. equipment and/or labour) were also part of the social networks, albeit to a minor extent (25.8% of all connections were based on material exchange only). The size of the networks differed significantly between the four types of owners (P = 0.008). Members who had harvested had a significantly greater average network size compared to that of non-members who had not harvested (Table 3a). With respect to harvesting-related issues, network size, on average, was smaller, i.e. 4.5 persons/organisations. A similar trend was noted to that for general forestry-related issues, i.e. members who had harvested had the largest average network size (5.1) and non-members who had not harvested had the smallest social network with a mean of 2.7 (Table 3b).

1 − ∑i ai bi

(Newman, 2003). The terms eij and eii express the number of connections between forest owners (i) and others in the network (j) according to whether they hold a profit interest or not as fractions of the total number of connections (Table 2a). For the data shown in Table 2a, b shows how r is calculated. The value of the coefficient r can lie between −1 and 1. Negative values indicate disassortative mixing, where persons connect with others that have different attributes; positive values indicate assortative mixing, representing situations where persons connect with others that have similar attributes. Where there is no correlation, the assortativity coefficient has a value of 0. Descriptive statistics were calculated for the size and diversity of social networks and the nature of the connections (e.g. harvesting-related influence). Because of the non-normal distribution of the data and the

Table 2 Example of calculation of assortativity coefficient regarding profit interest in forestry of members who had harvested presented in Table 7. (a) Connections expressed as fractions of total amount of connections (146) Alters

Forest owner

Profit yes Profit no b

Profit yes

Profit no

a

75/146 = 0.5137 9/146 = 0.0616 0.5753

54/146 = 0.3699 8/146 = 0.0548 0.4247

0.8836 0.1164

(b) Calculation of Newman's assortativity coefficient ∑i eii = (0.5137 + 0.0548) = 0.5685

ai = ∑j eij = (0.5137 + 0.3699) = 0.8836 & (0.0616 + 0.0548) = 0.1164 bj = ∑i eij = (0.5137 + 0.0616) = 0.5753 & (0.3699 + 0.0548) = 0.4247 ∑i ai bi = 0.8836 ∗ 0.5753 + 0.1164 ∗ 0.4247 = 0.5578

r=

∑i eii − ∑i ai bi 1 − ∑i ai bi

= (0.5685–0.5578)/(1–0.5578) = 0.0242

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Table 3 Network size of participants' social networks (a) for forestry-related issues in general (b) for harvesting-related issues. All participants (n = 56)

Harvested

Not harvested

P-value

Members (n = 24)

Non-members (n = 16)

Members (n = 9)

Non-members (n = 7)

(a) Network size Network size range

7.4 2–20

8.7a 2–20

7.4ab 3–12

6.2ab 4–8

4.7b 2–9

0.008

(b) Network size Network size range

4.5 1–15

5.8a 1–15

4.1a 1–8

3.3a 1–6

2.7a 1–5

0.064

a,b

Means followed the same letter are not significantly different at the P < 0.05 level.

with private consultants/managers and family/friends, but less so with Teagasc and the Forest Service (Table 4b).

Table 4 Proportion of participants that named at least one person in each category as a source of information or work (a) for forestry-related issues in general (b) for harvesting-related issues.

3.3. Network diversity Harvested

(a) Forest service Teagasc Private consultant/ manager Logger Family/friends Staff Member (b) Forest service Teagasc Private consultant/ manager Logger Family/friends Staff Member

Not Harvested

Members (n = 24)

Nonmembers (n = 16)

Members (n = 9)

Nonmembers (n = 7)

0.42 0.88 0.88

0.63 0.34 1.00

0.22 0.78 1.00

0.28 0.57 1.00

0.58 0.88 0.67 0.79

0.50 0.94 n/a n/a

0.56 0.78 1.00 0.22

0.14 0.86 n/a n/a

0.21 0.67 0.75

0.19 0.31 1.00

0.22 0.22 0.22

0.00 0.14 0.86

0.58 0.67 0.63 0.75

0.50 0.56 n/a n/a

0.44 0.67 1.00 0.22

0.14 0.57 n/a n/a

Ego-centric social networks can also be characterised by the diversity of the persons/organisations in them. Using the categories shown in Table 1, and all forestry-related contacts, the mean number of categories in the social networks of the forest owners was 4.3 (n = 56) (Table 5a). Members were in contact with a maximum of 7 different categories, non-members dealt with a maximum of 5 different categories, as there is no Staff or other Members. The diversity of the social networks of the four types of owners differed significantly (P < 0.001; Table 5a). Members who had harvested had significantly more categories in their networks compared to those of non-members who had harvested, and non-members who had not harvested (Table 5a). Similar results were found regarding harvesting-related networks; members who had harvested had significantly more categories in their networks compared to those of non-members who had harvested and non-members who had not harvested (Table 5b). The IQV (Index of Qualitative Variation) values of forestry-related social networks of the four types of owners differed significantly (P = 0.023; Table 5a). The social networks of members who had harvested were more heterogenic than the social networks of non-members who had not harvested (Table 5a). Similarly, the harvesting-related social networks of four types of owners also differed significantly (P = 0.012); the IQV for the social networks of members who had harvested was significantly greater than that for non-members who had not harvested (Table 5b).

3.2. Network composition The most common forestry-related contacts for all of the forest owners were private consultants/managers and family/friends (Table 4a). Teagasc was also a popular contact for most of the types of owners with the exception of non-members who had harvested; a greater proportion of this owner type were in contact with the Forest Service. Forest owners who were members of an owner group were more often in contact with other members if they had harvested. However, all participants had harvesting-related contact most commonly

3.4. Influence and trust While the social networks of forest owners can comprise a number of persons/organisations, the influence of these persons/organisations on the forest owners can vary. Indeed, some may have no influence at all. Similarly, the number of connections/ties between a forest owner

Table 5 Diversity of participants' social networks (a) for forestry-related issues in general (b) for harvesting-related issues. All participants (n = 56)

Harvested

Not Harvested

P-value

Members (n = 24)

Non-members (n = 16)

Members (n = 9)

Non-members (n = 7)

(a) Diversity (no. of different categories) Diversity (IQV)

4.3 0.78

5.1a 0.83a

3.6b 0.73a

4.6ab 0.83a

2.9b 0.68a

< 0.001 0.023

(b) Diversity (no. of different categories) Diversity (IQV)

3.2 0.63

4.1a 0.76a

2.6b 0.58ab

3.0ab 0.65ab

1.7b 0.27b

0.001 0.012

a,b

Means followed the same letter are not significantly different at the P < 0.05 level.

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hidden interests of the other person.

Table 6 Strength of influence by categories on the participants' forest-related decisions and participants' average perception of trust by categories. No. of ties

Forest service Teagasc Private consultant/ manager Logger Family/friends Staff Member

Forest service Teagasc private consultant/ manager Logger Family/friends Staff Member a b

Average of influence ratesa

Average of trustb

No. of ties

Average of influence ratesa

Average of trustb

(a) Members & harvested (n = 24)

(b) Non-members & harvested (n = 16)

10 21 69

11 6 56

32 40 16 21

0.067 0.245 0.125

0.117 0.156 0.464 0.323

0.300 0.619 0.275

0.406 0.400 0.688 0.524

12 34 n/a n/a

0.091 0.388 0.176

0.017 0.098 n/a n/a

2 7 19

3 4 13

5 12 9 2

0.000 0.125 0.333 0.000

0.200 0.417 0.556 0.000

1 12 n/a n/a

0.000 0.158 0.308

0.880 0.000 n/a n/a

‘Well I trust my neighbour obviously because they weren't going to benefit one way or the other. It was just generous sharing of information and they were very, very helpful’. In contrast, persons or organisations that do benefit commercially by providing advice (e.g. those who might advise an owner to thin their plantation while benefitting from a percentage of the sales value) were trusted less by the forest owners.

0.250 0.324 n/a n/a

(d) Non-members & not harvested (n = 7)

0.500 0.429 0.263

For most of the forest owners interviewed, trusting was easier when the person who gave advice did not commercially benefit by it. The general perception was that this was the case with forest owner groups, neighbours, and Teagasc.

0.364 0.667 0.339

(c) Members & not harvested (n = 9) 0.000 0.214 0.000

‘I would always question their motivation. Not their knowledge, but their motivation, why they are telling [me] to go a particular route’.

‘I'm not sure how much I would take their advice because they are all coming at it from a business point of view. They're all representing somebody who's in it to make money’. Regarding harvesting issues, the trust issue was directed towards the right selection of trees for harvesting, or the reluctance among some to provide estimates of timber sales prices.

0.667 0.500 0.462

‘The difficulty with thinning is … you'd better know who you're bringing in there. Because there's no guarantee of what they'll bring out – in terms of: will they help themselves? … unless you're up there watching them, or unless you trust them’.

1.000 0.333 n/a n/a

‘And you hear of people not getting the right costs for things, you know? … You see, the industry is delighted to be able to bamboozle the farmer because he can then get away with telling you little porky pies’.

0 = no influence, 1 = strong influence. 0 = no trust, 1 = trust.

In cases where forest owners trust specific persons or organisations, this had three possible fundaments: (a) trust was either based on shared interests – including profit interests – between the forest owner and the other person; (b) forest owners tended to trust those persons who they dealt with persistently over time, or (c) who they also socialised with in situations beyond forestry.

and the various categories of persons/organisations in the network is not a good indicator of influence as this number is highly affected by the potential number of persons/organisations within a category. For example, some categories, e.g. Forest Service, have only approximately four persons operating within the study area while the Private Consultant/Manager category has many more. Hence having a relatively large number of connections with a category may only be a reflection of the greater potential choice within that category. Thus in this study the influence towers generated by the forest owners were used to provide insight into the strength of influence of those in the social networks. For general forestry-related issues (Table 6), members, on average, considered Staff to be the most influential, while non-members described Private Consultants/Managers and Teagasc as the most influential category. While the number of ties between forest owners and Private Consultants/Managers was the highest among all categories for all four owner types, these were not always very influential except for non-members (Table 6b and d). The high average influence value for loggers among non-members who had not harvested (i.e. 0.880) arises from the one forest owner in this owner type who was connected to a logger classing this connection as highly influential. The person/organisations that were most influential were also often perceived to be the most trustworthy (Table 6), with members of forest owner groups perceiving the staff of the group to be the most trustworthy. Among non-members, Teagasc and the Forest Service were considered the most trustworthy. As was the case with “influence” the high value for trust associated with the logger arises from the one forest owner from the non-member type that had not harvested classing this connection as trustworthy. In the following section, direct quotes from the forest owners are given to give greater insight into the quantitative scores of trust. In general, forest owners addressed trust less as a matter of doubting the knowledge of others and instead addressed it in the context of the

‘I think he is an honest kind of a guy and it's very important that whoever you employ is there for your best interests and I think they are’. ‘I trust him because we are living beside him for years’. ‘They're farmers, like myself, like … I mean, there's nobody going to tell you lies when you can find out the truth the next day’. Most persons/organisations, though, were not distrusted by the forest owners. ‘So I would say, trust, not really. But don't take that as mistrust. I'm not saying any of them is dishonest. I'm just saying they all have a particular job to do for a particular company, whose job is ultimately to make money - that's what business is all about’.

Table 7 Assortativity coefficients (r) regarding interests in forestry. Harvested

Profit Environmental protection Development of forestry

6

Not harvested

Members (n = 24)

Non-members (n = 16)

Members (n = 9)

Non-members (n = 7)

0.0242 0.0501 −0.0644

−0.0808 0.1095 −0.0514

0.0000 0.0000 −0.0073

0.0000 −0.1342 −0.1538

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‘They're a private company. But you're not going to spend your life not dealing with private companies. You know? I trust him as much as I trust the person I bought this car from. Yeah, they're in it for the profit as well. But I'd hope that they'd sell a half-decent car, you know? So, any company, you can only trust them a certain amount’.

likely that they meet other members there and that they talk to them. It is also likely that forest owners include information that they got from other members in their decision-making (Rickenbach, 2009). As highlighted above brief and loose contacts are often forgotten when it comes to creating one's own social network and is a known weakness of selfreported social networks. The alternative approach to identifying social networks is to conduct a survey, in which all individuals in the network are surveyed. With this approach the issue of forgetting brief and loose contacts is avoided, however, a high response rate is needed for such an approach to ensure that the network does not remain with large or many holes in it (Wasserman and Faust, 1994; Robins, 2015).

3.5. Assortativity Table 7 presents assortativity coefficients (r) for the four types of forest owner and the three interests. The assortativity coefficients were all close to 0 suggesting neither assortative mixing nor disassortative mixing among any of the four types of owner.

4.3. Network diversity

4. Discussion

Much more robust than the number of individual persons in the network is the estimation of the number of categories of persons in the network (Sagor and Becker, 2014) as the diversity of a social network plays an important part in the decision-making process. Different people introduce different ideas, and the more varied those other people are, the more novel the information that a person bases their decisions on (Granovetter, 1973; Burt, 1992). Eventually, this diversity in contacts is associated with a more active forest management (Sagor and Becker, 2014). Such theories and observations suggests that the relatively high diversity of the networks of members of forest owner groups who have harvested will result in greater exposure to more diverse forestry-related information, which may lead ultimately to higher harvesting activity. Yet, it is not clear whether a more active harvesting behaviour leads to higher network diversity, or whether a more diverse network leads to increased harvesting activity. The causality only becomes explicit by understanding how a diverse network evolves. The traditional descriptive Social Network Analysis that was used in this study does not have the means to tackle the underlying causal mechanism. It needs a tool (within Social Network Analysis) that tests which initial connections or attributes lead to diverse networks that are eventually observed.

4.1. Network size The social networks of members who have harvested were the largest ones; those of non-members who have not harvested were the smallest ones. Compared to other studies (Knoot and Rickenbach, 2011 (mean = 4.1); Kittredge et al., 2013 (mean = 8.6); Sagor and Becker, 2014 (mean = 2.92); Ruseva et al., 2014 (mean = 3.6)) the average size of the forest owners' networks was relatively large. It reveals the forest owners' dependence on other sources of information and assistance and reflects the lack of forestry-related knowledge and training among Irish private forest owners. Given the self-reporting nature of the process used, the number of persons the participants identified in the network should not be interpreted as the complete list for a number of reasons. First, because when self-reporting, people tend to forget persons that are not well embedded in an experience (e.g. an unusually good or bad relationship, or an ongoing interaction) (e.g. Brewer, 2000). Nevertheless, the persons/ organisations that were identified can be interpreted as relatively strong or stable contacts (Wasserman and Faust, 1994, p. 57; Brewer, 2000). Second, participating owners often perceived family members as joint forest owners rather than someone they only talked to or worked with in forestry. It was observed in the study that forest owners, although not mentioning family members when naming people they talk or work with, often referred to “we” when talking about ownership in the course of the interview (e.g. “we manage it on our own, we don't have any other help”); when asked who they meant by “we”, they mentioned family members. Thus, such family members would be additional persons in the social networks, yet are not pictured in the data that were collected.

4.4. Trust and influence Participants stated that they trust persons in forestry less when they benefit commercially by their advice. Reports about trust for forestry professionals vary throughout literature. Forest owners in Finland trusted those professionals that they could easily communicate with or had positive experience with (Hujala and Tikkanen, 2008); most Swedish forest owners view information from professionals that was given in person as trustworthy (e.g. Kindstrand et al., 2008). In contrast, scepticism of forestry professionals and harvesting contractors has been noted in the United States (Rickenbach, 2009; Ruseva et al., 2014), where the staff and other members of a forest owner group are the most trusted source of information (Rickenbach, 2009). The categories that were trusted most by the participants in the present study were Teagasc, the forest owner group and family/friends/neighbours. Apart from vested interests, forest owners also based trust on the continuity of the contact with a person or with a contact not associated with forestry. This agrees with Granovetter's (1985) argument that it is personal relations in a social network that generate trust in economic situations. Dealing with a person over a long period of time encourages trust in particular as there is interest in carrying on future economic dealings. Where the relationship is not only of economic nature, but overlaps with a social relationship, this encourages trust and predictable behaviour even more (ibid.). Granovetter (1985) also points out that being part of a social network discourages cheating as the cost of a damaged reputation is greater than the gain of cheating. According to the author (ibid.), the information about another person's reputation is not equally important for one's own decision; it depends where the information comes from: Generally known information about another person's reliability is used for decision-making when nothing else is

4.2. Network composition Private consultants/managers and family/friends were among the most contacted categories regarding general forestry-related as well as harvesting-related issues. Members who had not harvested were in contact with the Staff of the group more than with other members. The contacts to the Staff and other Members were more balanced among members who had harvested. This low proportion of forest owners who contacted other members seems to contradict the findings of other studies which suggest a key role of forest owner groups in peer-to-peer learning (e.g. Ma et al., 2012; Kueper et al., 2013). However, Rickenbach (2009) found forest owner group members had less contact with other members of a forest owner association than they had with professional foresters, both staff of the association and professionals outside it. He explained this relatively low contact with other members as a consequence of the difficulty to recall persons that the participant does not have stable contacts with (as outlined above in relation to Network Size). In this study, 92% of members indicated that field trips were an important service of the owner group. Assuming most of those members that highlighted this actually do attend field trips, it is very 7

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available; information about a person's reliability obtained from a trusted person is more important than generally or commonly known information. This can help explain why Teagasc and the staff of the group are highly trusted; at organised meetings forest owners meet each other and share information about the forest owner group's and Teagasc's reliability; hence trustworthy information about the group's or Teagasc's reputation is more readily available. Teagasc and the forest owner group staff were also those categories that were rated by the participating forest owners as most influential on forestry-related decisions. Trust is critical in taking-up information, i.e. in using the information that is given to forest owners for their decisionmaking (Hujala and Tikkanen, 2008). Contact with trusted persons can increase the knowledge of a forest owner and can lead to adoption of certain management behaviour (e.g. Rogers, 2003; Kueper et al., 2013). Trust as an element in knowledge transfer has been neglected so far in any publications regarding Irish forestry; the results here should encourage further investigation of the role of trust in relationships of forest owners.

owners indeed reaches them, and influences their decision-making. Such findings can guide the design of knowledge transfer strategies to encourage sustainable wood mobilisation; i.e. strategies that acknowledge the importance of relationships between forest owners and peers, professional foresters, contractors and others, and which support opportunities for creating those relationships should be promoted. However, further research is required to address how effective forest owner groups are at bringing about the desired action, i.e. harvesting, among the forest owners.

4.5. Assortativity

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Acknowledgements This work was supported by the European Commission's 7th Framework Programme [grant agreement No. 3613762]. The authors also appreciate the insights of Maarten Nieuwenhuis and Thomas Grund who had excellent suggestions for improvement. References

The analysis of the connections formed using the assortativity matrices and coefficients did not provide any evidence that the forest owners were making connections with persons/organisations based on their interests in forestry. While assortativity is a commonly used element in Social Network Analysis in general, it is largely absent from forestry-related literature. Patterns can only be found when more studies include assortativity in their methodological design. Further research is also needed to determine what attributes influence the formation of forestry-related connections; additionally, it would be important to determine whether the making of connection is due to a decision of the forest owner (be it implicit or explicit) to connect with persons that do have that particular attribute. 5. Conclusion Literature increasingly reports that social interaction plays a relevant role in decision-making in relation to natural resources (e.g. Prell et al., 2009; Knoot and Rickenbach, 2011; Kueper et al., 2013). However, studies examining the role of social networks in forest management are rare. This exploratory study provides the first insights into the social networks of forest owners in Ireland and the results suggest an association between these networks and the harvesting activity of forest owner, although the direction of the association is not clear. To address this causation versus connection conundrum and to answer if and why social networks of forest owners are linked with harvesting behaviour a social network study should be conducted using an approach that would account for time to help understand how social networks – their structure and attributes – evolve. It is only by observing a complete network at several points in time, regarding its structure and the behaviour of the individuals in the network, that strong causal statements can be made. The vast majority of the connections in the analysed social networks of the forest owners were for information exchange highlighting the role of social networks in knowledge transfer. The results imply that knowledge transfer is not about mere information-giving, but about connecting with people and building a relationship (which includes e.g. trust) so that information can flow. As the number of new forest owners increases throughout Europe and as the traditional knowledge of forestry among owners declines, the transfer of forest management knowledge will become more important. Finding an effective means of knowledge transfer to and among this new cohort of owners is a challenge that needs to be addressed urgently. The results suggest that forest owner groups are at least as effective as public technical assistance (such as that provided by Teagasc and the Forest Service); where effective in this context implies that information that is aimed at forest 8

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