Agriculture around the world is affected by systemic risks, including production, market, financial, organisational, personal, and climatic challenges. To adapt to these risks and improve productivity, farmers rely on both formal and informal social networks. Through relationships and collaborations with peer farmers and agricultural experts, specialists, and organisations, farmers benefit not only from strong ties of trust and mutual support, but also from access to advanced scientific knowledge, infrastructural and financial resources and support through weaker ties with external actors. Along with this, the social network of agricultural organizations plays a vital role in facilitating farmers’ access to knowledge and resources and to support them in facing risks and issues. Thus, analysing the structure and characteristics of these networks and identifying key actors involved in policymaking and developing strategies that strengthen farmers’ resilience and adaptation is crucial. This study examines the social network of farmers and agricultural organisations involved in pistachio production in Central Iran. Pistachio is among the most significant agricultural products in this region, playing a significant role in economy, society, and culture of this area. However, producers face diverse risks, particularly water shortage and market-related challenges. By identifying the systemic risks affecting pistachio production and applying both qualitative and quantitative analysis of social network of local farmers and organisations, this study aims to provide insights into how different networks influence farmers’ adaptation to those risks and issues and their productivity.

Prior research indicates that in developing countries where agriculture is economically significant, links between farmers are important for transmitting information and generating social capital and support. This is especially the case in rural Ethiopia, which is also one of the countries most at-risk from climate change. To study how linkages between farmers impact adaptation behavior and climate resilience, an egocentric network survey using snowball sampling was launched in four Ethiopian kebeles (local administrative divisions) to study informational and support networks. The survey is part of a larger follow-up survey to a baseline household questionnaire conducted in additional kebeles. Results from the baseline survey and a geospatial model indicate that household responsiveness to livelihood shocks is highly localized, perhaps indicating a role for village networks in supporting resilience. In the network survey, we find important differences between kebeles in terms of access to extension services and output markets. We also find that, while informational and support networks are topologically similar within kebeles, cross-kebele differences are notable. In the next steps of the project, I simulate full networks from our partial network data, and then will use an agent-based model of diffusion to study how adaptation strategies propagate differently through these various networks. This work contributes to a growing literature on the importance of networks to learning and capacity-building in sub-Saharan Africa, as well as the relevance of such networks to climate change adaptation. Our results will also help to inform interventions targeting at-risk households and communities.

This paper explores the knowledge network and flow within the seed treatment sector, focusing on the development of alternative biological solutions for seed protection in response to the French Regulation’s 2018 EGALIM law (Article 83), which prohibits the use of unapproved plant protection substances. The purpose of this paper is to examine how diverse stakeholders, including seed producers, research laboratories, and biocontrol companies, collaborate to develop innovative biological seed treatments, addressing the complexity of this innovation that spans seed, seed technology, and biological sectors.

The study models the networks connecting stakeholders within Vegepolys Valley, a French innovation cluster specializing in plant breeding and agricultural solutions. Tools like RStudio and UCInet are used to quantify knowledge flows, with metrics such as centrality and density assessing the influence of individual actors and the cohesiveness of the network. The analysis also identifies key knowledge brokers and gatekeepers who control and facilitate information flow, shaping innovation pathways. The study further examines how knowledge moves between clusters within France, Europe, and globally.

The contributions of this paper are twofold. First, it provides a unique focus on biological seed treatment as a complex, multi-sector innovation involving collaboration across the seed, seed technology, and biological industries. Second, it applies quantitative methods to analyze knowledge flow within a heterogeneous cluster, emphasizing the economic and organizational dimensions of seed treatment innovation. This offers new insights into how regional clusters foster sustainable agricultural solutions in response to regulatory changes.

Periodic monitoring of pests and diseases in crops is essential for early detection, enabling preventative agrotechnical interventions while minimizing treatments in disease-free areas (Jeger et al., 2018). However, monitoring is often hindered by resource limitations, leading to delayed detection and unaddressed outbreaks, resulting in excessive pesticide use, escalating costs, environmental damage, and risks to worker and consumer health (Savary et al., 2019). This study employs social network analysis (SNA) to model the spatiotemporal dynamics of biotic stressors in two distinct agroecosystems (Garrett et al., 2018).

We investigated two systems: [1] the two-spotted spider mite (TSSM, Tetranychus urticae), affecting screenhouse-grown sweet peppers (Capsicum annuum) in southern Israel (Weintraub & Palevsky, 2008); and [2] white mold (WM, Athelia rolfsii), damaging open-field peanuts (Arachis hypogaea) in northern Israel (Dafny-Yelin, 2022). These systems represent contrasting environments and pathogen types, allowing for comparison across agricultural contexts.

During 2015–2017, TSSM abundance was monitored weekly in four pepper screenhouses by examining every 20th plant in every fifth row . In 2024, seven WM-infected peanut fields were monitored weekly or bi-weekly using a 20 × 1 m grid system.

To construct network models, each monitored field divided into grid. Each grid cell represented by a node. Directed edges (e_ij) between nodes indicated pathogen detection in grid cell i at time t followed by detection in grid cell j at time t+1 (Parry et al., 2014). Edge weights were calculated as the inverse distance between cells, reflecting proximity's influence on transmission probability (Sanatkar et al., 2015).

Statistical analysis using exponential random graph models (ERGMs) revealed probabilities of edge formation between grid cells (Robins et al., 2007). The models incorporated spatial covariates. Other covariates such as environmental parameters, and crop-specific factors can be added (Silk et al., 2017).

Results showed both common patterns and system-specific differences. In both networks, infection spreading to western grid cells had lower probability (p < 0.01). However, in TSSM networks, edge probability increased with distance, suggesting long-range dispersal capabilities possibly aided by human movement or equipment (Skirvin & Fenlon, 2003); conversely, in WM networks, edge probability decreased with distance, aligning with soil-based transmission through mycelial growth and localized sclerotia germination (Xu et al., 2012).

Network centrality measures identified 'hotspot' locations disproportionately influencing overall infection dynamics (Pautasso et al., 2010).

This research demonstrates the potential of applying SNA methodologies to agricultural crop protection (Shaw & Pautasso, 2014), offering a framework for optimizing sampling strategies by identifying high-risk locations and transmission pathways, enabling more efficient resource allocation in pest and disease management (Cunniffe et al., 2015).

Agri-food systems in Latin America face critical challenges arising from demographic exploitation, climate change, and migration. To address these, it is essential to understand the social structure embedded in these complex systems. However, there is a knowledge gap in how power dynamics, collaboration, and reciprocity within these networks affect the efficiency of these systems. This study addresses this gap by combining the Social Network Analysis method and "usable past" approach to assess the impact of CGIAR’s “AgriLAC Resiliente” Initiative implemented in Honduras, Guatemala, Colombia, Mexico, and Peru, on the transformation and configuration of collaborative networks in the agricultural sector. This approach allows understanding structural properties of the network by analyzing the relationships between actors before the intervention, which we used as a baseline for evaluating changes in the structure, connectivity, and collaboration dynamics among actors. The results show a significant expansion of the network, with increased participation of actors and connections, which has facilitated a more efficient flow of information, reduced distances between actors, and increased transitivity, suggesting better collaboration, knowledge generation, and innovation. Challenges include low network density and decreased reciprocity, highlighting the need to promote more equitable exchanges and deepen actor integration to strengthen long-term resilience. We provide empirical evidence on how network-based interventions facilitate exchange, collaboration, and innovation in agri-food systems to respond to major challenges, along with recommendations for improving the dynamics in the social structure of agri-food systems. These results can be used to inform public policies oriented at promoting more equitable and sustainable collaboration.

Social-ecological networks, such as seed exchange networks (SEN), allow examining resilience in farming systems. These networks are relational structures that foster agrobiodiversity within farming systems, thereby enabling farmer communities to cope with broader social-ecological changes. This study contributes to the understanding of SEN and their role in farming resilience by exploring local seed exchange dynamics in a territory recognized for its high agrobiodiversity in the southern Andes of Chile. Using a relational approach including qualitative and quantitative methods, we co-constructed a SEN from a sample of 80 homegardens tended by campesinos and lifestyle migrants who have recently arrived in this territory. We examined a set of network properties that have been implicated with farming resilience, including density, modularity, and centrality. Furthermore, we used egocentrically exponential random graph models (ego-ERGMs) to examine if (i) seeds were actors configuring the observed exchange interactions, and (ii) whether the origin of people engaged in small-scale farming, along with the diversity of exchanged crop varieties and their functional traits, may contribute to homophilous tendencies in subgroup formation within this network. We observed a decentralized and fragmented SEN connecting 559 campesinos and migrant homegarden tenders. We found that homegarden tenders and seeds are coupled actors shaping seed exchange interactions and subgroup formation in SEN. Despite this result, we did not find any homophilous tendency based on the crops’ functional traits. Our results provide evidence that diversity, across both social and ecological scales, is critical to foster seed exchange interactions and farming resilience.

In the semi-arid tropics of India, agrarian distress, farmer suicides, livelihood precarity, and entrenched caste-driven social stratification persist amidst underdeveloped or inaccessible formal financial systems. Limited non-farm livelihood options heighten dependence on agriculture, making social networks vital for resilience (Patnaik, 2010). Using household-level data from Kanzara village on social networks, input use, labor patterns, and agricultural output, we explore how these networks mitigate risks and enhance productivity in a climate-vulnerable, resource-constrained context. Social networks foster trust-based cooperation, enabling farmers to pool resources, share timely information on weather and inputs (Pratiwi & Suzuki), and access labor during critical periods, thereby reducing transaction costs and liquidity constraints (Barnett-Howell & Mobarak, 2021). However, caste-based hierarchies stratify network access, often excluding marginalized groups from high-yield knowledge or equipment-sharing arrangements, perpetuating inequities. This study examines how social networks—centered on credit access, agricultural information sharing, farm equipment exchange, and labor collaboration—influence input decisions, labor allocation, and productivity. It also investigates how caste-based hierarchies shape these networks and affect who benefits from resource sharing. This research highlights the role of social networks in mitigating farming risks where formal institutional support, such as credit markets or extension services, is limited. These findings offer valuable insights for policies aimed at strengthening agricultural resilience and sustainability in similar agrarian settings.

References

Barnett-Howell, Z., & Mobarak, A. M. (2021). Social networks analysis in agricultural economies. In Handbook of Agricultural Economics (Vol. 5, pp. 4613-4652). Elsevier.

Patnaik, I. (2010). Distress situation in dryland areas impacts on livelihood pattern and the coping strategies: A review. Research Unit for Livelihoods and Natural Resources.

Pratiwi, A., & Suzuki, A. (2017). Effects of farmers’ social networks on knowledge acquisition: Lessons from agricultural training in rural Indonesia. Journal of Economic Structures, 6, 1-23.

Smallholder farmers critically depend on seed access. It is especially true, in regions experiencing significant interannual precipitation variability, where access to a broad diversity of locally-adapted seed varieties is key for agricultural systems resilience.

However, a general assessment of the drivers of seed access is lacking. This study investigates the processes through which smallholder farmers source seeds in challenging environmental conditions, examining the socioeconomic, cultural, and geographical drivers of seed sourcing patterns. It examines how the structure and composition of seed circulation networks influence farmers' equitable access to seeds, and identifies the key drivers of inequality.

The research is based on a comparative analysis across three geographically and socioculturally distinct study sites in Senegal, Madagascar, and Morocco. These sites were selected due to their diverse agroecological conditions and institutional frameworks, while also sharing similarities in farming systems and crop diversity management. We analyzed data collected through surveys in 2,000 household surveys, documenting crop diversity at both species and varietal levels, agricultural management practices, socioeconomic indicators, and seed sourcing networks.

The study: (1) applies multivariate approaches and cluster analysis to identify households profiles based on their seed sourcing practices; and (2) uses social network analysis to identify key network actors, structural configurations, and relational dynamics that influence seed access opportunities for households.

This assessment across these three countries will allow to identify, if share patterns exist, which will be useful to deliver practical recommendations for improving equity in seed access.

Ecological networks model species-to-species interactions, and are intended to be predictive models for an ecosystem. When inferring ecological networks from observational data, we assume precision and recall, i.e., that (1) a network link reflects a true pairwise functional relationship between species, and (2) all true relationship are modelled as links. Unfortunately, for opaque ecosystems such as the soil, with numerous and microscopic species, functional information is rare. Instead, co-occurrence networks are inferred by sampling the soil. We ask the question: how accurate are these spatial networks of microorganisms, as inferred with current soil-sampling methods?

An agent-based model with biologically realistic behaviour and parametrisation simulates a plot of land, with true trophic links between species. We observe the spatial co-occurrence that these trophic links naturally produce in space. We also simulate the taking of individual samples from this spatial distribution of species. Finally, we evaluate the accuracy of the co-occurrence network inferred from samples, against the true co-occurrence of the plot.

We find that biological properties other than the interactions, such as the species diversity in the plot, can be estimated with relatively low error by sample pooling. On the other hand, the inference of the co-occurrence network is poor. We see high errors of the pairwise link weights, with large mean errors, and large standard deviations between experiments. The co-occurrence network inferred is thus both inaccurate and unstable (explaining the large differences seen among algorithms for co-occurrence inference), and this is intuitively explainable in spatial terms.

Germany is exploring collective approaches to agri-environmental and climate schemes (cAECS) to enhance ecological, economic, and social effectiveness. This study examines key actors, their roles and responsibilities, and their interconnections to inform institutional design, particularly concerning ‘collective elements’ (e.g. participatory design, collective planning, involvement of third-party intermediaries). To do so, we integrate Social Network Analysis (SNA) with the Institutional Analysis and Development (IAD) framework. Using the Net-Map method, we collect data from three early-implementation cAECS cases in Germany that vary in biophysical conditions (farming and ecosystem characteristics), social attributes (farm sizes and social structures), and rules-in-use (funding sources and contract types).

Our results show how these contextual differences influence the structure of collective networks as well as the presence of ‘collective elements’. Intermediaries serving as coordinating actors emerge as a central feature of these networks, bearing most of the administrative workload, while others assume supporting roles. Trust-building during the initiation phase is identified as a critical factor for fostering long-term collaboration and further integration of collective elements. Additionally, variations in contract types and farm sizes shape both the design and implementation requirements of collective approaches. Despite these differences, all cases demonstrate the potential for significant benefits from adopting a collective approach to agri-environmental and climate scheme governance. Examples of such benefits include the adoption of measures on a landscape scale, the reduction of transaction costs and the increase in social cohesion.

The EU’s agri-food sector both drives and remains vulnerable to environmental risks. Despite increasing calls for greening, EU agri-food policies continue to prioritise the interests of agricultural policy elites, such as farmer associations. However, the strategies these elites employ to resist much-needed reform remain underexplored. While the existence of closed policy networks is well-documented in (post-)exceptionalism scholarship, less is known about how policy elites obstruct or resist reform. Drawing on politicisation literature, we contend that protest-related discourse can amplify politicisation to a level where reform opportunities shrink, effectively blocking change. Using Discourse Network Analysis of Euractiv articles, we examine protest-related discourse on sustainable agriculture during a period of reform momentum (2018-September 2024). Our preliminary findings reveal a steady increase in politicisation, which only subsided after the 2024 European Parliamentary elections. A turning point in 2022-2023 saw agricultural policy elites frame sustainability as a threat to food security, escalating discourse polarisation and right-wing actors infiltrating protests. This marginalised environmental reform efforts and legitimised the rollback, weakening, and withdrawal of greening initiatives. By heightening politicisation, policy elites reinforced exceptionalist legacies and productionist interests, keeping policy networks closed and obstructing sustainability reforms.