Conceptual implications

herbi-cides have resulted in the contamination of ground water and neighboring food crops, and have been identified as a cause of severe health problems (Bopp 2016; Board 2019; Rujivanarom 2020), partic-ularly among wage laborers who are hired for the application of agro-chemicals on large-scale plan-tations, lacking adequate knowledge and equipment for self-protection.

Most affected by the detrimental social and environmental consequences of sugarcane farming are poor households, which benefit least from it. Even better-off households which have the capacity to transform and profit from cash-crop production in the short term, might find themselves confronted with increasing economic and environmental adversity and declining household resilience in the long term. Accordingly, the detrimental social and environmental consequences of sugarcane farming underline that economic growth does not automatically lead to higher levels of resilience and serves as an illustrative example of how growth-oriented state interventions “[…] have done more to extract people from the countryside and from farming than they have to support rural living and farm-based livelihoods” (Rigg and Salamanca 2009, p. 261).

With regard to the question of what role migration-related translocal networks plays in the resilience of rural livelihoods, patterns of households’ network capital point to the fact that poor households in particular critically rely on migration-related translocal bonding capital as a financial coping mecha-nism. However, as discussed above, among poor households remittances from migrating household members and relatives are likely to be less resourceful sources of support and are likely to be less strategically invested than among better-off households. At the same time, poor households, in the context of the delocalization of rural livelihoods, are more affected by the erosion of local bonding capital, which leads to a situation in which migration gains in terms of remittances are compensated by migration costs in terms of agricultural wage labor. Accordingly, findings suggest, the plight of the poor does lies not only in the fact that they lack extra-local and bridging capital (Woolcock and Narayan 2000), but also in a one-sided overreliance on migration-related bonding capital in combi-nation with less effective local capital.

6.4.2 Agricultural diffusion research

Based on the assessment of the structural features and key actors in translocal advice-sharing net-works, this study has underlined that while agricultural advice sharing is a prevailing local phenom-enon, translocal networks matter for agricultural innovation. Research findings underline the fact that agricultural innovation networks are far from being self-contained (Matous 2015; Matous and Todo 2018) and hence confirm the call to more seriously address the spatiality of innovation pro-cesses (Binz et al. 2014; Glückler et al. 2017). At the same time the findings challenge the popular conceptualization of networks as conduits for the contagion of novel information and knowledge that underlies agricultural innovation research (Valente and Rogers 1995; Conley and Udry 2001) and also informs translocality research (Greiner and Sakdapolrak 2013a, 2013b).

Besides reiterating that adoption of agricultural innovation faces multiple social, economic, and cul-tural barriers, and depends on a wide array of factors, with several of them being distinctive features of smallholder farming (Feder et al. 1985; Feder and Umali 1993; Llewellyn and Brown 2020) the assessment of translocal innovation networks points to the fact that innovation transfers are more complex than the simplistic notion of contagion through advice sharing suggests. In particular in rural communities with poorly developed advice-seeking cultures, observation of peers is of critical relevance for the local spread of innovations, as it is related to physical proximity and the visibility of agricultural plots. As this study reveals, however, observation is not restricted to the local level but also matters during migration. Network assessment building solely on the assessment of communica-tion networks regarding agricultural issues (e.g. Isaac 2012; Isaac et al. 2014) therefore risk missing out these more subtle mechanism of contagion.

Besides the lack of consideration of barriers and farming conditions and an overly simplistic concep-tualization of the nature of innovation transfers, a further misconception lies in the assumption that migration and translocal networks facilitate the transmission of “new” information and knowledge (Scheffran et al. 2012; Sakdapolrak et al. 2016; Peth and Sakdapolrak 2020b). While studies on iso-lated agricultural communities suggest that long-distance ties are of particular importance to agricul-tural innovation, because they connect across different agro-ecological conditions (Wossen et al.

2013; Matous and Todo 2018), this study underlines that, in highly connected and integrated rural communities, the likelihood of learning something factually new about agriculture through migra-tion-related translocal networks is relatively limited. In the context of high outreach of agricultural extension services and media coverage, as is the case in Northeast Thailand, it is less the novelty than the relative advantage of an agricultural innovation that is decisive for smallholders’ adoption deci-sions (Llewellyn and Brown 2020).

Finally, the idea of “knowledge gain” as the decisive driver of agricultural change also ought to be questioned. As the analysis of particular key actors in translocal innovation networks suggests, the impact of migration on agricultural innovation lies not so much in the acquisition of knowledge about an improved crop or practice as is the exposure to unfamiliarity and previously unknown environ-ments. This assessment corresponds with increasing empirical evidence that migration not only re-sults in incorporated social remittances (e.g. knowledge, skills) but also in intangible social remit-tances (e.g. views, values, motivation) (Peth and Sakdapolrak 2020a). As discussed above, in the ob-served study site, the confidence and motivation of particular return migrants to make a change to their agricultural livelihoods is a decisive factor for agricultural change.

6.4.3 Translocality research

According to the above outlined consideration regarding the conceptualization of social capital and the diffusion of agricultural innovations, for translocality research the following conceptual implica-tions can be drawn:

In order to contextualize the role of translocal social networks, translocality studies ought to shift their focus on translocal relations towards the interaction between local and translocal networks. As outlined above, understanding the full impact of translocal networks on livelihood resilience requires the consideration of translocal as well as local networks. In the same vein, translocality studies ought to move from a preoccupation with migration-related networks comprised of strong and reciprocal household or family ties, towards accounting for formal translocal networks as well. In the context of rural transformation, the formalization of social relations is gaining importance and hence should be considered regarding its impact on translocal livelihoods.

Furthermore, this study has made the point that, in order to better understand mechanisms of agri-cultural innovation, translocality research ought to refrain from overly simplistic assumptions about the diffusion of innovations, which are implicit in the connectionist conceptualization of translocal social networks. A narrow focus on the flow of knowledge and ideas via networks of direct social interaction might miss other important pathways of innovation. Additionally, the assumption of

“novel knowledge” and “knowledge gain” as deciding factors in adoption decision runs the risk to downplay social, economic, and cultural reasons for adoption. Against this backdrop, translocality research ought to account for the interplay of financial and social remittances, in particular intangi-ble social remittances, such as skills, belief and motivations, and the constraining or incentivizing effects of the socio-economic and cultural environment. Peth and Sakdapolrak (2019) provide a con-ceptual model of the transfer of social capital, which could provide a qualitative framing for further structural assessments of translocal innovation networks.

In order to better employ the potential of the translocality concept for understanding the interrela-tion between migrainterrela-tion and changes in the physical and natural environment (Greiner and

Sakdapolrak 2013b), a further issue that ought to be embraced by translocality research is the role of social networks in natural resource management (Bodin and Prell 2011). The application of SNA in the strand of natural resource governance has yielded plentiful and detailed insights into the ques-tion of what structural features foster the transformaques-tion towards resilience in SES. These insights however have remained unnoticed by migration and translocality research. Although the issue of natural resource governance has not been in the focus of empirical studies presented by this study, strengths and weaknesses were systematically analyzed in the form of the preceding literature re-view (Article I). Against this backdrop, the author confidently argues that exchange beyond discipli-nary boundaries would offer the opportunity to overcome limitations and blind spots on both sides;

whether the metaphorical and connectionist conceptualization of social networks and the focus on

primary scale of resource use and the tendency to downplay conflicts and the role of power asymme-tries in governance networks. The integration of a connectionist perspective on flows of remittances between migrants and receiving households with a structuralist perspective on coordination pro-cesses between stakeholders involved in natural resource management provides a promising path-way for understanding resilience not only at the actor and household level, but also at the level of the related SES in areas of origin and destination.