Global and local knowledge networks spread regenerative agriculture in Europe

Global and local knowledge networks spread regenerative agriculture in Europe | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global and local knowledge networks spread regenerative agriculture in Europe Loekie Schreefel, Pablo Tittonell, Emile Steenman, Fabian Adler, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7519599/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Agriculture supports human well-being but exerts pressure on the environment, driving climate change and biodiversity loss. Regenerative agriculture is gaining traction as a solution, yet how farmers access knowledge about regenerative practices remains unclear. This study interviewed 128 self-identified regenerative farmers across five European countries, identifying 834 unique knowledge sources spanning over 52 distinct practices. Using Social Network Analysis, we show that both national and international knowledge sources play pivotal roles. Peer-to-peer learning dominated nationally, while prominent international figures (e.g. Richard Perkins and Allan Savory) shaped broader narratives across borders through diverse channels, including books and digital platforms. This suggests international knowledge sources tend to inspire guiding principles, while local actors adapt practices to context. Our findings highlight the importance of hybrid knowledge spaces that connect scientific and farmer expertise, strengthen multi-scale networks through intermediaries, and ensure digital platforms offer evidence-based, context-informed content to scale regenerative agriculture effectively. Earth and environmental sciences/Ecology/Agroecology Earth and environmental sciences/Environmental social sciences/Sustainability Regenerative farming social network analysis social media food production sustainable development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The global food system is a cornerstone of human survival and well-being, yet its environmental footprint is vast. Agricultural production exerts substantial pressure on planetary boundaries, contributing to biodiversity loss, climate change, and land-system change 1 . Alarmingly, six of the nine planetary boundaries have already been breached, including those related to greenhouse gas emissions, biodiversity loss, freshwater use, and biogeochemical flows 2 . The food system alone accounts for approximately one-third of global anthropogenic greenhouse gas (GHG) emissions, is a major driver of terrestrial acidification, and significantly contributes to eutrophication in surface waters 3,4 . In response, a growing number of sustainable agricultural approaches have been proposed to reconcile food production with ecological integrity. Regenerative agriculture has gained traction among food system actors - including policymakers, agribusinesses, and farmers - as a pathway to restore soil health, enhance biodiversity, and mitigate climate change 5–8 . The concept has moved rapidly from the margins to the mainstream as nearly 90% of the world’s top 100 food companies now endorse regenerative agriculture, collectively committing more than $3.2 billion in investments 9 . At COP28 10 , 26 agrifood actors pledged to transition 160 million hectares of land to regenerative agriculture by 2030, engaging 3.6 million farmers and allocating over €2 billion in funding. Despite this momentum, the implementation of regenerative agriculture in Europe remains slow and strongly influenced - if not dominated - by agri-food corporations 11–13 . Although these agri-food corporations are not directly engaged in farming, their dominant market position enables them to significantly influence upstream value chain processes, including on-farm management decisions. This raises critical questions about how regenerative farmers access and utilize knowledge when adopting regenerative practices. Current research worldwide provides limited insight into how knowledge about regenerative agriculture diffuses within farming communities, yet understanding these knowledge flows is key to scaling regenerative agriculture 6,14,15 . In this context, it is important to recognize that, as with several other alternative approaches to farming, knowledge about regenerative agriculture often circulates through farmer-led and self-organized networks, rather than through formal institutional channels 16 . Social networks are central to farmers' decision-making, shaping their adoption of new practices, fostering innovation, and enhancing resilience 17–19 . While previous studies highlight the importance of social relations in agricultural knowledge diffusion 20,21 , little is known about the specific knowledge sources that regenerative farmers rely on or the key actors driving knowledge diffusion. Understanding these networks - by identifying influential knowledge sources, uncovering the mechanisms through which knowledge is diffused, and assessing the role of trust – can help inform the design of effective knowledge diffusion strategies that could accelerate the transition to regenerative agriculture. This study addresses these gaps by employing Social Network Analysis (SNA) to examine the knowledge networks of regenerative farmers across five European countries—Germany, the Netherlands, France, Spain, and Portugal (hereafter referred to as Europe). Drawing on 128 semi-structured interviews with self-identified regenerative farmers, it investigates where they source knowledge on regenerative practices, the types of practices (e.g., tillage, composting) and sources they consult (e.g., other farmers, social media), the trust placed in these sources, and the frequency of interaction. By mapping key knowledge flows and social dynamics, this research provides critical insights for academic discourse and policy design, which could strengthen efforts to support farmers and scale regenerative agriculture across Europe. 2. Results 2.1 Farm descriptive information The majority (56%) of the 128 self-identified regenerative farmers interviewed across five countries in Europe were crop-oriented (Fig. 1a), primarily horticulturists (n=30) and arable farmers (n=22). A considerable proportion (33%) were mixed crop-livestock farmers, whereas specialized livestock farmers accounted for a smaller share (12%), including dairy (n=14), beef (n=2), and pig farmers (n=2). These shares were similar across the case-study countries, though certain regional trends emerged. In Spain and Portugal, perennial systems such as agroforestry (n=30) and orchards (n=27) were more common, whereas in Germany and the Netherlands, horticulturists (n=18) and arable farmers (n=12) were more prevalent. Overall, most respondent farmers (54%) managed very small to small farms. As illustrated in Fig. 1b, the majority of farmers had less than 10 years of farming experience (SD=13.6), and, on average, they began identifying as regenerative farmers after approximately five years (SD=6.2). 2.2 Farm-centric network analysis The SNA of the 128 farmers responses revealed a knowledge network comprising 834 distinct sources related to 52 aggregated agricultural practices (Fig. 2a). Among these, 62 respondent farmers were identified as sources of knowledge, with 10 mentioned by other respondent farmers (in-degree=12), while 58 identified themselves as sources of knowledge (in-degree=58). Moreover, one farmer relied exclusively on personal experience and self-experimentation. On average, each farmer obtained knowledge from 6.5 sources (SD=5.2; Supplementary Table 1). The actor roles most frequently cited as sources of knowledge included fellow farmers (20%, actor role: farmer(s) + interviewed farmers without self-citation), authors (19%, e.g., of books), and research or educational institutions (19%) (Supplementary Table 2). Additionally, specific individuals emerged as central figures in knowledge diffusion across Europe, characterized by both high in-degree and betweenness centrality scores. The most prominent among these were Richard Perkins, Allan Savory, and Joel Salatin. Richard Perkins, a market gardener based in Sweden with significant outreach through social media, exhibited an in-degree of 32. Allan Savory, originally from Zimbabwe but in this study most associated to the US, is widely known for his work in holistic management and regenerative grazing systems (in-degree=29), while Joel Salatin, based in the United States, has gained prominence through his advocacy of sustainable farming and his published works (in-degree=28). A list of the top-100 knowledge sources cited is provided in Supplementary Table 3. Among the 1380 edges representing knowledge obtained by farmers about different agricultural practices, knowledge was most frequently sourced about regenerative agriculture in general (21%), agroforestry-style systems (8%), and tillage reduction or avoidance (3%). A comprehensive overview is provided in Supplementary Table 4. Notably, 20% of knowledge sources provided knowledge on multiple practices (Fig. 2). Furthermore, the frequency of interaction between farmers and their knowledge sources varied considerably. While 19% of farmers reported engaging with their sources on a weekly basis, 42% indicated rare interactions, typically occurring annually or less. However, infrequent interactions did not necessarily equate to lower perceived importance, as trust in knowledge sources remained high: more than 90% of sources were rated with either moderate or strong trust levels. More information on frequency and trust levels is provided in Supplementary Table 5 and 6. National-level networks play a key role in regenerative knowledge diffusion, despite the coexistence of supra-national actors across European countries, as depicted in Fig. 2b where only knowledge sources with an in-degree greater than two are shown (96 nodes). At the European level, the knowledge network exhibited a moderate degree of centralization (0.034). In contrast, national-level networks varied significantly, ranging from highly decentralized structures in France (0.013) to highly centralized configurations in Portugal (0.072). Spain (0.049) and Germany (0.047) showed moderate centralization, while the Netherlands aligns with the European average (0.034). These results indicate that some national knowledge networks rely heavily on a few dominant sources, whereas others share knowledge more evenly among many sources. In total, the knowledge actors originated from 33 different countries (Fig. 3a), however, on average, 56% (SD=29) of all citations refer to national actors, which further indicates that national knowledge sources regarding regenerative practices are key. Yet, certain key knowledge sources transcend national boundaries, facilitating knowledge diffusion on a global scale. In particular, the United States emerges as a critical hub for knowledge diffusion (in-degree=293), with influential figures such as Allan Savory, Joel Salatin, and Gabe Brown playing a pivotal role. More information on country specific use of national and international use of knowledge sources is given in Supplementary Fig. 2 and 3. Within national settings, peer-to-peer learning among farmers (26%,) and advisory services (18%) were the most frequently utilized knowledge sources (Fig. 3b). When accounting for secondary actor roles - for instance, when an individual was primarily identified as a book author but secondarily as a farmer - 47% of all citations referred to peer farmers. National sources were particularly sourced for specific practices such as agroforestry, crop diversity, and cover crops, while international sources primarily provided knowledge on regenerative practices in general. In contrast, the latter were predominantly accessed through authors (36%). Overall, most sources did not provided knowledge about single practices to farmers but about multiple practices. Among the 21 knowledge sources that were most frequently cited by farmers across Europe, Fig. 4a shows the countries in which these top actors were cited, while Fig. 4b illustrates the types of regenerative practices for which each actor was referenced. The data reveal that some sources were predominantly cited by farmers operating in countries that have similar agroecological or cultural contexts, whereas others were referenced more broadly across diverse regions. For instance, Dietmar Nässer, based in Germany, was cited exclusively by farmers from Germany and the Netherlands. In contrast, Joel Salatin from the United States was referenced by farmers in both Western and Southern European countries. Additionally, certain sources - such as The Biggest Little Farm - were primarily used for general knowledge on regenerative agriculture. Others, including Richard Perkins and Allan Savory, were cited in relation to specific practices such as reduced tillage and holistic grazing, respectively. 3. Discussion The transition to regenerative agriculture involves more than just the adoption of alternative practices at the field level as it is deeply embedded within dynamic knowledge networks that shape perceptions, enable information flows, and ultimately influence decision-making. These networks - comprising actors ranging from local farmers to international NGOs, book authors, scientists, and corporate entities - are central to the promotion, credibility, and diffusion of regenerative practices. Here, we explore the multifaceted role of these knowledge networks, the evolving nature of peer-to-peer learning, and the challenges of ensuring that the knowledge shared is credible, contextually relevant, and inclusive. 3.1 The role of knowledge networks in regenerative agriculture The success of new approaches to sustainable agriculture is closely tied to the efficiency, accessibility, and credibility of knowledge diffusion 18,20–22 . Diverse actors - farmers, researchers, NGOs, and corporate actors - play distinct yet complementary roles at disseminating and legitimizing knowledge. Farmer-led networks, such as EARA (European Alliance for Regenerative Agriculture), are particularly valuable in translating abstract concepts into context-specific, actionable insights. These networks foster trust and promote learning through practical, lived experiences that resonate strongly with local peers. Their centrality, however, remains less prominent than that of highly influential individuals and book authors (cf. Fig. 2). Our study confirms the continued importance of peer-to-peer learning 23 , with farmers citing other farmers accounting for 47% of the 834 knowledge sources identified. At the same time, nearly half of the farmers reported to also rely on their own observations and experimentation, positioning themselves as sources of knowledge. This practice of self-experimentation - conducted independently from formal institutional frameworks - reflects an alternative pathway to agricultural innovation 24–27 . While such an approach allows for adaptation to local contexts, it also raises a crucial question: how can this knowledge be effectively shared within the wider farming community? This issue is mirrored in the structure of the European network under study, where only 12 citations referred to respondent European farmers who self-identified as “regenerative”. The observed limited degree of interconnection among farmers within the sample, despite the wider recognition of the central role of farmers in knowledge circulation and innovation, may stem from both methodological limitations and contextual variables. For instance, not all self-identified regenerative farmers were included in the study (46%), thereby limiting the number of connections made in our knowledge networks. Limited interconnections could also be due to competition between farmers, the arguably little experience farmers have with regenerative agriculture (on average 5 years), or due to fragmented terminology. This study focused only on farmers who self-identified as regenerative, thereby excluding those identifying with overlapping approaches such as agroecology or organic farming. Since these approaches often share similar practices 11 , more inclusive knowledge networks that emphasize practices and outcomes rather than labels (e.g. agroecology or regenerative agriculture) could foster broader collaboration across the sustainable agriculture community. The role of non-farming actors within knowledge networks remains equally critical. Scientific actors contribute evidence-based insights that reinforce the empirical foundations of regenerative agriculture by aligning practices with ecological, agronomic, and economic data 28 . However, some practices promoted within regenerative agriculture - such as compost teas or certain bio stimulants for nitrogen-fixing promoted by industry - have raised concerns for lacking efficacy or robust evidence 29 . While trust in science remains relatively high among farmers, accessibility issues - including academic jargon, paywalls, and disciplinary silos - can hinder the broader uptake of scientific knowledge 30 . Advisors, educational programs, and NGOs serve a vital bridging function by translating scientific research into actionable knowledge for farmers, policymakers, and the public. Peer-farmers and advisors appeared as the most prominent national sources of knowledge, while authors and education actors were most often mentioned as international knowledge sources (cf. Fig. 3b). This may suggest that international sources contribute mainly to the inspiration of principles, while local actors are more involved in the adaptation of practices. Value chain businesses (e.g., processors or retailers) are increasingly engaging in regenerative agriculture, motivated by sustainability imperatives and market opportunities 13 . While their involvement brings capital and scaling potential, our results show that they remain marginal in farmers' learning processes. This raises questions about the ability and willingness of these actors to contribute to the diffusion of regenerative knowledge, revealing a possible gap between global value chain narratives of commitment and local learning dynamics experienced by farmers 13 . 3.2 The evolving nature of peer-to-peer learning Traditionally, peer-to-peer learning among farmers was facilitated through in-person interactions - farm visits, demonstration fields, and agricultural fairs 18 . While these forms of exchange remain foundational, they are increasingly being supplemented - or even replaced - by books and digital platforms 31 , including social media 32 , forums, and virtual training 33 . This evolution has contributed to shifting peer-to-peer learning towards more trans-local forms of learning. For instance, the eight most-cited actors in our study did not operate within the farmers’ case countries, and seven were located on different continents. This finding highlights the global reach of books and digital platforms and the emergence of new influencer-led learning ecosystems. Farmers now follow figures such as Ernst Götsch (on e.g. agroforestry), Allan Savory (on e.g. holistic grazing), and Richard Perkins (on e.g. reduced tillage) on YouTube and Instagram, often adopting practices showcased from entirely different agroecological contexts. While this digital shift democratizes access to knowledge, it also introduces new uncertainties. Practices advocated by global influencers may be universally appealing but require careful adaptation to local conditions to ensure their effectiveness 34 . The Savory Institute, for example, has attempted to address this challenge by establishing regional knowledge hubs to adapt pasture and grazing management techniques to specific bioregions. However, such infrastructure is lacking for many other influential sources, raising questions about the applicability of globally shared practices in diverse local contexts. There is a risk that knowledge diffusion through social media influencers ends up replicating the approach of ‘one-size-fits-all’, which has characterized agricultural knowledge systems since the onset of the green revolution 35 . 3.3 Challenges in regenerative agriculture knowledge networks Despite the dynamism of regenerative agriculture knowledge networks 36,37 , several critical challenges persist. One of the most pressing is the risk of misinformation or oversimplification, particularly within digital spaces 38 . The rapid and viral nature of social media allows anecdotal claims - such as exaggerated promises of carbon sequestration or immediate yield increases - to spread widely without adequate scientific validation. While digital tools enhance access, they also blur the lines between verified knowledge and speculative or untested practices. Beyond issues of accessibility, tensions over the legitimacy of knowledge also arise between different knowledge paradigms. For instance, farmers may distrust research that does not reflect their lived experience 39 , while scientists may dismiss anecdotal evidence as unquantifiable 29 . These tensions are compounded by the different demands placed on knowledge by various actors: policymakers require scalable, evidence-based metrics for systemic change, NGOs often advocate for urgent action based on ethical imperatives, and agribusinesses must balance sustainability with profitability. Navigating these sometimes-conflicting imperatives requires a co-production approach to knowledge, wherein farmers, researchers, NGOs, policymakers, and corporate actors collaboratively engage in research design, implementation, and evaluation 25 , thereby increasing knowledge credibility, saliency and legitimacy 27 . In addition, there are significant gaps in the regenerative agriculture knowledge landscape. Economic viability remains underrepresented in most regenerative agriculture literature and discourses 13,40 . With notable exceptions 5 , most case studies on regenerative agriculture focus on ecological outcomes, while fewer examine the profitability, labour implications, and market access realities that ultimately determine whether farmers can adopt and sustain regenerative practices. Without clear, localized data on financial sustainability, the transition to regenerative agriculture may remain accessible only to well-resourced or ideologically driven farmers. Policy integration is another critical barrier. Although regenerative agriculture aligns well with long-term sustainability goals, it often exists outside current policy and subsidy frameworks, particularly within the EU's Common Agricultural Policy (CAP). This lack of institutional alignment creates disincentives, especially for conventional farmers who might be open to transitioning but lack the necessary guidance or economic support. 3.4 Toward a more effective and inclusive knowledge ecosystem To strengthen the role of knowledge networks in the adoption and scaling of regenerative agriculture, a multifaceted approach is required. First, supporting hybrid knowledge spaces is recommended: promoting co-creation of knowledge that integrates scientific rigor with farmer experience can enhance both credibility and practical relevance. Second, strengthening multi-scale connectivity is needed to build linkages across local, national, and international networks, thereby ensuring that global strategies remain grounded in local realities. Intermediaries such as NGOs and extension services play a crucial role at facilitating these exchanges. Third, digital platforms should be leveraged responsibly, with careful curation and management to promote the dissemination of balanced, evidence-informed, and context-sensitive information. Establishing mechanisms for content verification and transparent sourcing can help mitigate the risks of misinformation. In conclusion, while knowledge networks have immense potential to drive the transformation toward regenerative agriculture, realizing this potential depends on how these networks are structured, governed, and made accessible. Addressing existing challenges and leveraging digital and institutional tools thoughtfully can foster more resilient, inclusive, and impactful knowledge ecosystems for a regenerative future. 4. Methods To explore the knowledge diffusion dynamics that guide the implemented practices of self-identified regenerative farmers we focused on five case-countries across Europe: Germany, the Netherlands, France, Spain, and Portugal. These countries were selected because they feature a diverse range of farming systems in Southern and Western Europe, with contrasting environmental challenges, socio-economic conditions and political contexts 41,42 . Self-identified regenerative farmers were contacted and those who agreed were interviewed (Fig. 5). Their responses were structured into a database following a node and edge list format to perform SNA. The following sections provide a detailed overview of each methodological step. 4.1 Data collection To identify regenerative farmers, we utilized the Regenerative Actor Database compiled in 2024 by Schreefel et al. 13 , which includes all food system actors (n=849) with a demonstrated commitment to regenerative agriculture in the case-study countries. Of these, 281 self-identified regenerative farmers were identified in the database and contacted via email, phone, and social media to participate in the study. A total of 128 farmers agreed to participate, resulting in a response rate of 46% (Supplementary Table 7). The study included 23 farmers from Germany, 30 from the Netherlands, 18 from France, 46 from Spain, and 11 from Portugal. Data collection was conducted through semi-structured interviews between April and November 2024, with an average interview duration of one hour. Interviews followed a semi-structured protocol based on a two-section questionnaire (see Supplementary Questionnaire for details) and were conducted concurrently across the five case-study countries by co-authors fluent in the respective local languages. The interviews collected data in four key areas. First, farmers provided descriptive information about themselves and their farms via Microsoft Forms, including details such as years of farming experience, years practicing regenerative agriculture, farm type, and farm size. Farm sizes were benchmarked on a five-point scale from very small to very large against national inventories (Supplementary Table 8). Second, using Microsoft Excel, farmers listed the regenerative practices implemented on their farms (e.g., cover cropping, no-tillage). Third, they identified the sources of knowledge that informed each practice and specified the role (e.g., advisory services, other farmers) of each source. Fourth, farmers rated both the frequency in which they received knowledge and their level of trust in these sources. The frequency of knowledge received was assessed on a four-point scale: 1) rare - at most once per year; 2) occasional - at least twice per year but less than once per month; 3) frequent - at least once per month but less than once per week; 4) very frequent – at least once per week. Farmers also evaluated the trustworthiness of knowledge sources on a five-point trust scale: 1) strong distrust - deep mistrust of the source; 2) moderate distrust - some reservations about credibility; 3) neutral - neither trust nor distrust; 4) moderate trust - considerable confidence in reliability; 5) strong trust - high confidence in accuracy. However, as trust ratings were predominantly high, only frequency of knowledge received was retained for SNA (see Supplementary Table 5). 4.2 Social Network Analysis SNA was employed to analyse knowledge diffusion among the responding regenerative farmers. This approach considered recurring patterns of diffusion as social networks, wherein actors were represented as "nodes" in the network (Fig. 1), and the knowledge flow between respondent farmers and knowledge sources was depicted as "edges" 43,44 . Given the nature of farmers' networks, which generally involve numerous actors 18,45 , this study focused on egocentric, or farmer-centric, networks of respondent farmers to identify key sources of knowledge on specific regenerative practices. Consequently, only first-degree connections were considered, meaning that only edges between the respondent farmers (egos) and their knowledge sources (alters) were included. The data collected during the interviews were systematized into a node catalogue, detailing each actor’s name and node attributes, along with an edge list encompassing all ego - alter connections and their respective edge attributes. Networks were analysed and visualized using Gephi © 10 software 46 . Network graphs were generated to display nodes and their edges (Fig. 5), with directional edges indicated by arrows from the knowledge receiver node (respondent farmer) to the knowledge source node (source actor) 45 . Nodes were sized according to actors’ in-degree and coloured based on actors’ roles 18,47 . The actor roles were categorized into 10 groups: research or education; farmer(s); author; advisory; online content and media; respondent farmer; NGOs; value chain business; government agency, and other actors (see Supplementary Table 9 for category explanations). Edge thickness represented the strength of knowledge diffusion, corresponding to the frequency of received knowledge on a four-point frequency scale (i.e., rare to very frequent). Different edge colours indicated the practices being mentioned. As the practices cited by farmers exhibited varying degrees of overlap and specificity, they were aggregated into 52 clusters based on the methodology of Schreefel et al. 13 . For instance, some farmers referred to minimal tillage, others mentioned reduced ploughing, and some described specific tillage types (e.g., disk ploughing), depths, or frequencies. These variations were all clustered under minimal tillage (see Supplementary Table 10). Of these clustered practices, only the eight most frequently mentioned were used for visualization, while the remaining practices were grouped under the category "other practices". Additionally, a tenth category, "multiple practices", was introduced to group all edges involving exchanges on more than one of the nine primary clustered practices. For example, if a respondent farmer reported exchanging knowledge on “cover crops” and “crop diversity” with the same source, the parallel edges between the two nodes were merged under a single edge labelled "multiple practices", with their weights averaged. Duplicated edges were similarly merged with averaged weights. Network visualizations were constructed at two levels: 1) including all actors and edges, and 2) including only actors with an in-degree above two, with respondent farmers clustered by country of origin (i.e., Germany, the Netherlands, France, Spain, and Portugal). Gephi’s built-in metrics were used to compute both node and network level characteristics, allowing for the identification of knowledge diffusion and central actors providing knowledge to regenerative farmers. Node-level metrics included out-degree (number of edges emanating from a node, representing the number of sources cited by a farmer), in-degree (number of edges leading to a node, representing the number of times an actor was cited as a source of knowledge), and betweenness centrality (extent to which a node lays between other nodes) 45,48 . Further knowledge on these metrics and how these are quantified can be found in Supplementary Table 11. Key actors in knowledge diffusion were identified based on in-degree and betweenness centrality 18,47 . Actors with high in-degree were considered central to knowledge diffusion as they provided knowledge to many other actors 49 , while actors with high betweenness centrality were deemed crucial in controlling knowledge flow, acting as brokers or gatekeepers connecting otherwise disconnected actors 47 . Farmers’ utilization of knowledge sources was assessed based on out-degree, where a high out-degree indicated that farmers used a greater variety of knowledge sources 45 . At network level, in-degree centralization was computed to assess the extent to which knowledge diffusion was concentrated on a few dominant sources 49 . Declarations Data availability The processed datasets generated and analysed during the current study are available in the supplementary materials. Datasets containing privacy-sensitive information are not publicly available but can be obtained from the corresponding author upon reasonable request. Acknowledgements This study was funded by The Nest Family Office (TNFO). The funder played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Authorship LS: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, Funding acquisition. ES: Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization. RB: Validation, Formal analysis, Investigation, Data Curation, Writing - Review & Editing. FA: Validation, Formal analysis, Investigation, Data Curation, Writing - Review & Editing. 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Have farmers had enough of experts? Environ Manage 69 , 31–44 (2022). Schreefel, L., Schulte, R. P. O., de Boer, I. J. M., Schrijver, A. P. & van Zanten, H. H. E. Regenerative agriculture – the soil is the base. Glob Food Sec 26 , 100404 (2020). Guyomard, H. et al. Research for AGRI Committee – The next reform of the CAP: The variables in the equation. European Parliament, Policy Department of Directorate for Regional Development, Agriculture and Fisheries Policies, Brussels. (2024). Hartemink, A. E. Soil Atlas of Europe. J Environ Qual 35 , (2006). Haythornthwaite, C. Social network analysis: An approach and technique for the study of information exchange. Libr Inf Sci Res 18 , 323–342 (1996). Borgatti, S. P., Mehra, A., Brass, D. J. & Labianca, G. Network Analysis in the Social Sciences. Science (1979) 323 , 892–896 (2009). Simon, W. J., Krupnik, T. J., Aguilar-Gallegos, N., Halbherr, L. & Groot, J. C. J. Putting social networks to practical use: Improving last-mile dissemination systems for climate and market information services in developing countries. Clim Serv 23 , 100248 (2021). Bastian, M., Heymann, S. & Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. in Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009 361–362 (2009). doi:10.1609/icwsm.v3i1.13937. Dingkuhn, E. L., Sullivan, L. O., Schulte, R. P. O. & Grady, C. A. Navigating agricultural nonpoint source pollution governance : A social network analysis of best management practices in central Pennsylvania. PLoS One 19 , 1–30 (2024). Widayat, Y. Y., Karlina, N., Munajat, M. D. E. & Ningrum, S. Mapping Policy Actors Using Social Network Analysis on Integrated Urban Farming Program in Bandung City. Sustainability (Switzerland) 15 , (2023). Teodoro, J. D., Baird, J. & Otung, I. Longitudinal Network Analysis on a Farmers’ Community of Practice and Their Changes in Agricultural Systems Management. Soc Nat Resour 36 , 90–107 (2023). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementarymaterialsSNAEuropeR2.docx Supplementary information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7519599","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509218552,"identity":"4580b14e-745b-4ab1-b4b8-d2ef4d45226a","order_by":0,"name":"Loekie Schreefel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACPgYGZoYEEEsCRNgwyPBDJCxwamFD1ZLGwCPZwADn4taCUAPUYnCAkBb25sMGDyoY7Pln9z588CHBhsf4Ro7ZB8YdeLTwHEtOSDjDkDjjznFjwxkJaTxmN3KMZzCewaNFIsf4QGIbQ4KBRBqbNO+Pwzxmt3OMGRjb8GnJ/3wg8R+DPVAL++8/Cf95jGcT1JLDnJDYwMC4AWgLMOwO8BhIE9LCc8zYIOGYROKMG2nMkj0JyTwS958VMyTi0cLP3vxY8keNjT3/jDTGDz8S7OT4ew5vZvjYZoNTCxSgm5lASMMoGAWjYBSMArwAAGFMRjV6s9FtAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6794-7221","institution":"Wageningen University and Research","correspondingAuthor":true,"prefix":"","firstName":"Loekie","middleName":"","lastName":"Schreefel","suffix":""},{"id":509218553,"identity":"9b696f08-cffd-4921-8e86-6f322b37388e","order_by":1,"name":"Pablo Tittonell","email":"","orcid":"","institution":"Instituto de Investigaciones Forestales y Agropecuarias de Bariloche","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Tittonell","suffix":""},{"id":509218554,"identity":"97416fc0-2d42-493b-9bbd-cef79366c17d","order_by":2,"name":"Emile Steenman","email":"","orcid":"","institution":"Wageningen university \u0026 research","correspondingAuthor":false,"prefix":"","firstName":"Emile","middleName":"","lastName":"Steenman","suffix":""},{"id":509218555,"identity":"8a4f0f48-b6ca-4b5f-a542-61faece75753","order_by":3,"name":"Fabian Adler","email":"","orcid":"","institution":"Wageningen university \u0026 research","correspondingAuthor":false,"prefix":"","firstName":"Fabian","middleName":"","lastName":"Adler","suffix":""},{"id":509218556,"identity":"570da393-4154-40f1-9ba7-360009f2ea07","order_by":4,"name":"Ricardo Buffara","email":"","orcid":"","institution":"Wageningen university \u0026 research","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Buffara","suffix":""},{"id":509218557,"identity":"275c6e19-3b57-4493-a0a9-29b82a894358","order_by":5,"name":"Stephan Freundt","email":"","orcid":"","institution":"Wageningen university \u0026 research","correspondingAuthor":false,"prefix":"","firstName":"Stephan","middleName":"","lastName":"Freundt","suffix":""},{"id":509218558,"identity":"279f297f-3122-4f55-8c3d-973b586c2a43","order_by":6,"name":"Jeroen Groot","email":"","orcid":"https://orcid.org/0000-0001-6516-5170","institution":"Wageningen University \u0026 Research","correspondingAuthor":false,"prefix":"","firstName":"Jeroen","middleName":"","lastName":"Groot","suffix":""},{"id":509218559,"identity":"9bd09314-29d7-4ae3-ba6b-355754b7c8b0","order_by":7,"name":"Norman Aguilar-Gallegos","email":"","orcid":"","institution":"Universidad Panamericana","correspondingAuthor":false,"prefix":"","firstName":"Norman","middleName":"","lastName":"Aguilar-Gallegos","suffix":""}],"badges":[],"createdAt":"2025-09-02 16:05:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7519599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7519599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90570278,"identity":"c5adff28-214f-4b6a-80b6-83f8041ceaa0","added_by":"auto","created_at":"2025-09-04 08:15:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96503,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive information of the regenerative farmers interviewed. a) Distribution of farming system types and farm sizes per type (ranging from very small to very large) included in the study. b) Distribution of farming experience among interviewed farmers from different countries (DE: Germany, NL: Netherlands, FR: France, ES: Spain, PT: Portugal, and EUR: Europe as the average among the five case-countries), alongside the number of years they have practiced regenerative agriculture. The horizontal line inside each box represents the median, the dashed line indicates the average, and the whiskers show the minimum and maximum values excluding outliers. Data points beyond the whiskers are considered outliers.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7519599/v1/0e58855bb43b182d6149f842.png"},{"id":90570281,"identity":"9c256758-1d4a-4bb2-9f52-b375a9679dad","added_by":"auto","created_at":"2025-09-04 08:15:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":478491,"visible":true,"origin":"","legend":"\u003cp\u003eFarm-centric networks showing the diffusion of knowledge about regenerative practices. a) The complete knowledge network, displaying all nodes and edges, with labels restricted to the 20 actors with the highest in-degree. Nodes (bubbles) represent actors (interviewed farmers and knowledge sources), with node and label size indicating in-degree (i.e., how often a source was cited) and node colour denoting the type of actor. Regenerative practices are shown as edges (lines), with edge colour indicating the type of practice. b) Knowledge networks showing only knowledge sources (alters) with an in-degree greater than two (see also Supplementary Fig. 1), with all interviewed farmers (egos) merged by country of farm location: Netherlands (NL), Spain (ES), France (FR), Portugal (PT), and Germany (DE). Node size for knowledge sources reflects the in-degree, while for the five country nodes it reflects the number of farmers interviewed in each country. Edge thickness represents the frequency of knowledge sharing, and percentages in the legend indicate the proportion of nodes belonging to each category in the network.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7519599/v1/f889220aecad74de68140810.png"},{"id":90570280,"identity":"36b03653-d106-4ca7-b309-0eb07f8c0c53","added_by":"auto","created_at":"2025-09-04 08:15:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129650,"visible":true,"origin":"","legend":"\u003cp\u003eIn-degree of knowledge sources by country of origin, classified as national or international. a) In-degree of knowledge sources grouped by country, distinguishing between national and international origins. b) Most frequently cited actor roles as knowledge sources, shown by origin (national or international, L), and the regenerative practices (P) for which knowledge was obtained.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7519599/v1/9d26bc01cba57fb564b9f29a.png"},{"id":90570283,"identity":"d1999747-a746-400e-ac3f-47205033aad8","added_by":"auto","created_at":"2025-09-04 08:15:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139470,"visible":true,"origin":"","legend":"\u003cp\u003eTop 21 knowledge sources in Europe based on in-degree. Knowledge sources with an in-degree greater than six were included and are presented by country of origin (a) and by the specific regenerative practices for which they were cited (b). Country abbreviations following each knowledge source indicate origin: SW = Sweden, US = United States, UK = United Kingdom, CA = Canada, DE = Germany, BR = Brazil, JP = Japan, AT = Austria, AUS = Australia.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7519599/v1/b0dfeaab6dc1798e51f01f3a.png"},{"id":90570282,"identity":"3b5cb63d-0e17-4632-a6d6-c495f93cb47c","added_by":"auto","created_at":"2025-09-04 08:15:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":196307,"visible":true,"origin":"","legend":"\u003cp\u003eResearch methodology for assessing knowledge diffusion among regenerative farmers in Southern and Western Europe. The illustration depicts the research design used to examine knowledge diffusion processes. Regenerative farmers were selected from an existing database\u003csup\u003e13\u003c/sup\u003e, followed by semi-structured on-farm interviews to collect data on knowledge exchange. From this data, node and edge lists were generated and applied in a social network analysis. Farm locations are indicated as red dots on the map.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7519599/v1/310c6857f4b8721efa080aed.png"},{"id":91104395,"identity":"15357fd8-007a-402e-9aa7-24220b45293a","added_by":"auto","created_at":"2025-09-11 15:15:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1345494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7519599/v1/c9fa1549-dd5d-48e3-840b-73e42d2e793b.pdf"},{"id":90570284,"identity":"314bcf37-719b-47d2-a052-2bf5f640648f","added_by":"auto","created_at":"2025-09-04 08:15:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":390378,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"SupplementarymaterialsSNAEuropeR2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7519599/v1/0bae682c31d8f3fba9e542d1.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Global and local knowledge networks spread regenerative agriculture in Europe","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eThe global food system is a cornerstone of human survival and well-being, yet its environmental footprint is vast. Agricultural production exerts substantial pressure on planetary boundaries, contributing to biodiversity loss, climate change, and land-system change\u003csup\u003e1\u003c/sup\u003e. Alarmingly, six of the nine planetary boundaries have already been breached, including those related to greenhouse gas emissions, biodiversity loss, freshwater use, and biogeochemical flows\u003csup\u003e2\u003c/sup\u003e. The food system alone accounts for approximately one-third of global anthropogenic greenhouse gas (GHG) emissions, is a major driver of terrestrial acidification, and significantly contributes to eutrophication in surface waters\u003csup\u003e3,4\u003c/sup\u003e. In response, a growing number of sustainable agricultural approaches have been proposed to reconcile food production with ecological integrity. Regenerative agriculture has gained traction among food system actors - including policymakers, agribusinesses, and farmers - as a pathway to restore soil health, enhance biodiversity, and mitigate climate change\u003csup\u003e\u003cspan lang=\"EN-GB\"\u003e5\u0026ndash;8\u003c/span\u003e\u003c/sup\u003e. The concept has moved rapidly from the margins to the mainstream as nearly 90% of the world\u0026rsquo;s top 100 food companies now endorse regenerative agriculture, collectively committing more than $3.2 billion in investments\u003csup\u003e\u003cspan lang=\"EN-GB\"\u003e9\u003c/span\u003e\u003c/sup\u003e. At COP28\u003csup\u003e\u003cspan lang=\"EN-GB\"\u003e10\u003c/span\u003e\u003c/sup\u003e,\u0026nbsp;26 agrifood actors pledged to transition 160 million hectares of land to regenerative agriculture by 2030, engaging 3.6 million farmers and allocating over \u0026euro;2 billion in funding.\u003c/p\u003e\n\u003cp\u003eDespite this momentum, the implementation of regenerative agriculture in Europe remains slow and strongly influenced - if not dominated - by agri-food corporations\u003csup\u003e\u003cspan lang=\"EN-GB\"\u003e11\u0026ndash;13\u003c/span\u003e\u003c/sup\u003e.\u0026nbsp;Although these agri-food corporations are not directly engaged in farming, their dominant market position enables them to significantly influence upstream value chain processes, including on-farm management decisions. This raises critical questions about how regenerative farmers access and utilize knowledge when adopting regenerative practices. Current research worldwide provides limited insight into how knowledge about regenerative agriculture diffuses within farming communities, yet understanding these knowledge flows is key to scaling regenerative agriculture\u003csup\u003e6,14,15\u003c/sup\u003e. In this context, it is important to recognize that, as with several other alternative approaches to farming, knowledge about regenerative agriculture often circulates through farmer-led and self-organized networks, rather than through formal institutional channels\u003csup\u003e\u0026nbsp;16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSocial networks are central to farmers\u0026apos; decision-making, shaping their adoption of new practices, fostering innovation, and enhancing resilience\u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. While previous studies highlight the importance of social relations in agricultural knowledge diffusion\u003csup\u003e20,21\u003c/sup\u003e, little is known about the specific knowledge sources that regenerative farmers rely on or the key actors driving knowledge diffusion. Understanding these networks - by identifying influential knowledge sources, uncovering the mechanisms through which knowledge is diffused, and assessing the role of trust \u0026ndash; can help inform the design of effective knowledge diffusion strategies that could accelerate the transition to regenerative agriculture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study addresses these gaps by employing Social Network Analysis (SNA) to examine the knowledge networks of regenerative farmers across five European countries\u0026mdash;Germany, the Netherlands, France, Spain, and Portugal (hereafter referred to as Europe). Drawing on 128 semi-structured interviews with self-identified regenerative farmers, it investigates where they source knowledge on regenerative practices, the types of practices (e.g., tillage, composting) and sources they consult (e.g., other farmers, social media), the trust placed in these sources, and the frequency of interaction. By mapping key knowledge flows and social dynamics, this research provides critical insights for academic discourse and policy design, which could strengthen efforts to support farmers and scale regenerative agriculture across Europe.\u003c/p\u003e"},{"header":"2.\tResults","content":"\u003cp\u003e2.1 \u0026nbsp;Farm descriptive information\u003c/p\u003e\n\u003cp\u003eThe majority (56%) of the 128 self-identified regenerative farmers interviewed across five countries in Europe were crop-oriented (Fig. 1a), primarily horticulturists (n=30) and arable farmers (n=22). A considerable proportion (33%) were mixed crop-livestock farmers, whereas specialized livestock farmers accounted for a smaller share (12%), including dairy (n=14), beef (n=2), and pig farmers (n=2). These shares were similar across the case-study countries, though certain regional trends emerged. In Spain and Portugal, perennial systems such as agroforestry (n=30) and orchards (n=27) were more common, whereas in Germany and the Netherlands, horticulturists (n=18) and arable farmers (n=12) were more prevalent. Overall, most respondent farmers (54%) managed very small to small farms. As illustrated in Fig. 1b, the majority of farmers had less than 10 years of farming experience (SD=13.6), and, on average, they began identifying as regenerative farmers after approximately five years (SD=6.2).\u003c/p\u003e\n\u003cp\u003e2.2\u0026nbsp;\u0026nbsp;Farm-centric network analysis\u003c/p\u003e\n\u003cp\u003eThe SNA of the 128 farmers responses revealed a knowledge network comprising 834 distinct sources related to 52 aggregated agricultural practices (Fig. 2a). Among these, 62 respondent farmers were identified as sources of knowledge, with 10 mentioned by other respondent farmers (in-degree=12), while 58 identified themselves as sources of knowledge (in-degree=58). Moreover, one farmer relied exclusively on personal experience and self-experimentation. On average, each farmer obtained knowledge from 6.5 sources (SD=5.2; Supplementary Table 1). The actor roles most frequently cited as sources of knowledge included fellow farmers (20%, actor role: farmer(s) + interviewed farmers without self-citation), authors (19%, e.g., of books), and research or educational institutions (19%) (Supplementary Table 2). Additionally, specific individuals emerged as central figures in knowledge diffusion across Europe, characterized by both high in-degree and betweenness centrality scores. The most prominent among these were Richard Perkins, Allan Savory, and Joel Salatin. Richard Perkins, a market gardener based in Sweden with significant outreach through social media, exhibited an in-degree of 32. Allan Savory, originally from Zimbabwe but in this study most associated to the US, is widely known for his work in holistic management and regenerative grazing systems (in-degree=29), while Joel Salatin, based in the United States, has gained prominence through his advocacy of sustainable farming and his published works (in-degree=28). A list of the top-100 knowledge sources cited is provided in Supplementary Table 3.\u003c/p\u003e\n\u003cp\u003eAmong the 1380 edges representing knowledge obtained by farmers about different agricultural practices, knowledge was most frequently sourced about regenerative agriculture in general (21%), agroforestry-style systems (8%), and tillage reduction or avoidance (3%). A comprehensive overview is provided in Supplementary Table 4. Notably, 20% of knowledge sources provided knowledge on multiple practices (Fig. 2). Furthermore, the frequency of interaction between farmers and their knowledge sources varied considerably. While 19% of farmers reported engaging with their sources on a weekly basis, 42% indicated rare interactions, typically occurring annually or less. However, infrequent interactions did not necessarily equate to lower perceived importance, as trust in knowledge sources remained high: more than 90% of sources were rated with either moderate or strong trust levels. More information on frequency and trust levels is provided in Supplementary Table 5 and 6.\u003c/p\u003e\n\u003cp\u003eNational-level networks play a key role in regenerative knowledge diffusion, despite the coexistence of supra-national actors across European countries, as depicted in Fig. 2b where only knowledge sources with an in-degree greater than two are shown (96 nodes). At the European level, the knowledge network exhibited a moderate degree of centralization (0.034). In contrast, national-level networks varied significantly, ranging from highly decentralized structures in France (0.013) to highly centralized configurations in Portugal (0.072). Spain (0.049) and Germany (0.047) showed moderate centralization, while the Netherlands aligns with the European average (0.034). These results indicate that some national knowledge networks rely heavily on a few dominant sources, whereas others share knowledge more evenly among many sources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn total, the knowledge actors originated from 33 different countries (Fig. 3a), however, on average, 56% (SD=29) of all citations refer to national actors, which further indicates that national knowledge sources regarding regenerative practices are key. Yet, certain key knowledge sources transcend national boundaries, facilitating knowledge diffusion on a global scale. In particular, the United States emerges as a critical hub for knowledge diffusion (in-degree=293), with influential figures such as Allan Savory, Joel Salatin, and Gabe Brown playing\u0026nbsp;a pivotal role. More information on country specific use of national and international use of knowledge sources is given in Supplementary Fig. 2 and 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin national settings, peer-to-peer learning among farmers (26%,) and advisory services (18%) were the most frequently utilized knowledge sources (Fig. 3b). When accounting for secondary actor roles - for instance, when an individual was primarily identified as a book author but secondarily as a farmer - 47% of all citations referred to peer farmers. National sources were particularly sourced for specific practices such as agroforestry, crop diversity, and cover crops, while international sources primarily provided knowledge on regenerative practices in general. In contrast, the latter were predominantly accessed through authors (36%). Overall, most sources did not provided knowledge about single practices to farmers but about multiple practices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the 21 knowledge sources that were most frequently cited by farmers across Europe, Fig. 4a shows the countries in which these top actors were cited, while Fig. 4b illustrates the types of regenerative practices for which each actor was referenced. The data reveal that some sources were predominantly cited by farmers operating in\u0026nbsp;countries that have\u0026nbsp;similar agroecological or cultural contexts, whereas others were referenced more broadly across diverse regions. For instance, Dietmar N\u0026auml;sser, based in Germany, was cited exclusively by farmers from Germany and the Netherlands. In contrast, Joel Salatin from the United States was referenced by farmers in both Western and Southern European countries. Additionally, certain sources - such as The Biggest Little Farm - were primarily used for general knowledge on regenerative agriculture. Others, including Richard Perkins and Allan Savory, were cited in relation to specific practices such as reduced tillage and holistic grazing, respectively.\u003c/p\u003e"},{"header":"3.\tDiscussion","content":"\u003cp\u003eThe transition to regenerative agriculture involves more than just the adoption of alternative practices at the field level as it is deeply embedded within dynamic knowledge networks that shape perceptions, enable information flows, and ultimately influence decision-making. These networks - comprising actors ranging from local farmers to international NGOs, book authors, scientists, and corporate entities - are central to the promotion, credibility, and diffusion of regenerative practices. Here, we explore the multifaceted role of these knowledge networks, the evolving nature of peer-to-peer learning, and the challenges of ensuring that the knowledge shared is credible, contextually relevant, and inclusive.\u003c/p\u003e\n\u003cp\u003e3.1 The role of knowledge networks in regenerative agriculture\u003c/p\u003e\n\u003cp\u003eThe success of new approaches to sustainable agriculture is closely tied to the efficiency, accessibility, and credibility of knowledge diffusion\u003csup\u003e18,20\u0026ndash;22\u003c/sup\u003e. Diverse actors - farmers, researchers, NGOs, and corporate actors - play distinct yet complementary roles at disseminating and legitimizing knowledge. Farmer-led networks, such as EARA (European Alliance for Regenerative Agriculture), are particularly valuable in translating abstract concepts into context-specific, actionable insights. These networks foster trust and promote learning through practical, lived experiences that resonate strongly with local peers. Their centrality, however, remains less prominent than that of highly influential individuals and book authors (cf. Fig. 2). Our study confirms the continued importance of peer-to-peer learning\u003csup\u003e23\u003c/sup\u003e, with farmers citing other farmers accounting for 47% of the 834 knowledge sources identified. At the same time, nearly half of the farmers reported to also rely on their own observations and experimentation, positioning themselves as sources of knowledge. This practice of self-experimentation - conducted independently from formal institutional frameworks - reflects an alternative pathway to agricultural innovation\u003csup\u003e24\u0026ndash;27\u003c/sup\u003e. While such an approach allows for adaptation to local contexts, it also raises a crucial question: how can this knowledge be effectively shared within the wider farming community?\u003c/p\u003e\n\u003cp\u003eThis issue is mirrored in the structure of the European network under study, where only 12 citations referred to respondent European farmers who self-identified as \u0026ldquo;regenerative\u0026rdquo;. The observed limited degree of interconnection among farmers within the sample, despite the wider recognition of the central role of farmers in knowledge circulation and innovation, may stem from both methodological limitations and contextual variables. For instance, not all self-identified regenerative farmers were included in the study (46%), thereby limiting the number of connections made in our knowledge networks. Limited interconnections could also be due to competition between farmers, the arguably little experience farmers have with regenerative agriculture (on average 5 years), or due to fragmented terminology. This study focused only on farmers who self-identified as regenerative, thereby excluding those identifying with overlapping approaches such as agroecology or organic farming. Since these approaches often share similar practices\u003csup\u003e11\u003c/sup\u003e, more inclusive knowledge networks that emphasize practices and outcomes rather than labels (e.g. agroecology or regenerative agriculture) could foster broader collaboration across the sustainable agriculture community.\u003c/p\u003e\n\u003cp\u003eThe role of non-farming actors within knowledge networks remains equally critical. Scientific actors contribute evidence-based insights that reinforce the\u0026nbsp;empirical foundations\u0026nbsp;of regenerative agriculture by aligning practices with ecological, agronomic, and economic data\u003csup\u003e28\u003c/sup\u003e. However, some practices promoted within regenerative agriculture - such as compost teas or certain bio stimulants for nitrogen-fixing promoted by industry - have raised concerns for lacking efficacy or robust evidence\u003csup\u003e29\u003c/sup\u003e. While trust in science remains relatively high among farmers, accessibility issues - including academic jargon, paywalls, and disciplinary silos - can hinder the broader uptake of scientific knowledge\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdvisors, educational programs, and NGOs serve a vital bridging function by translating scientific research into actionable knowledge for farmers, policymakers, and the public. Peer-farmers and advisors appeared as the most prominent national sources of knowledge, while authors and education actors were most often mentioned as international knowledge sources (cf. Fig. 3b). This may suggest that international sources contribute mainly to the inspiration of principles, while local actors are more involved in the adaptation of practices. Value chain businesses (e.g., processors or retailers) are increasingly engaging in regenerative agriculture, motivated by sustainability imperatives and market opportunities\u003csup\u003e13\u003c/sup\u003e. While their involvement brings capital and scaling potential, our results show that they remain marginal in farmers\u0026apos; learning processes. This raises questions about the ability and willingness of these actors to contribute to the diffusion of regenerative knowledge, revealing a possible gap between global value chain narratives of commitment and local learning dynamics experienced by farmers\u003csup\u003e13\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2 The evolving nature of peer-to-peer learning\u003c/p\u003e\n\u003cp\u003eTraditionally, peer-to-peer learning among farmers was facilitated through in-person interactions - farm visits, demonstration fields, and agricultural fairs\u003csup\u003e18\u003c/sup\u003e. While these forms of exchange remain foundational, they are increasingly being supplemented - or even replaced - by books and digital platforms\u003csup\u003e31\u003c/sup\u003e, including social media\u003csup\u003e32\u003c/sup\u003e, forums, and virtual training\u003csup\u003e33\u003c/sup\u003e.\u0026nbsp;This evolution has contributed to shifting peer-to-peer learning towards more trans-local forms of learning.\u0026nbsp;For instance, the eight most-cited actors in our study did not operate within the farmers\u0026rsquo; case countries, and seven were located on different continents. This finding highlights the global reach of books and digital platforms and the emergence of new influencer-led learning ecosystems. Farmers now follow figures such as Ernst G\u0026ouml;tsch (on e.g. agroforestry), Allan Savory (on e.g. holistic grazing), and Richard Perkins (on e.g. reduced tillage) on YouTube and Instagram, often adopting practices showcased from entirely different agroecological contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile this digital shift democratizes access to knowledge, it also introduces new uncertainties. Practices advocated by global influencers may be universally appealing but require careful adaptation to local conditions to ensure their effectiveness\u003csup\u003e34\u003c/sup\u003e. The Savory Institute, for example, has attempted to address this challenge by establishing regional knowledge hubs to adapt pasture and grazing management techniques to specific bioregions. However, such infrastructure is lacking for many other influential sources, raising questions about the applicability of globally shared practices in diverse local contexts. There is a risk that knowledge diffusion through social media influencers ends up replicating the approach of \u0026lsquo;one-size-fits-all\u0026rsquo;, which has characterized agricultural knowledge systems since the onset of the green revolution\u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e3.3 Challenges in regenerative agriculture knowledge networks\u003c/p\u003e\n\u003cp\u003eDespite the dynamism of regenerative agriculture knowledge networks \u003csup\u003e36,37\u003c/sup\u003e, several critical challenges persist. One of the most pressing is the risk of misinformation or oversimplification, particularly within digital spaces\u003csup\u003e38\u003c/sup\u003e. The rapid and viral nature of social media allows anecdotal claims - such as exaggerated promises of carbon sequestration or immediate yield increases - to spread widely without adequate scientific validation. While digital tools enhance access, they also blur the lines between verified knowledge and speculative or untested practices.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond issues of accessibility, tensions over the legitimacy of knowledge also arise between different knowledge paradigms. For instance, farmers may distrust research that does not reflect their lived experience\u003csup\u003e39\u003c/sup\u003e, while scientists may dismiss anecdotal evidence as unquantifiable\u003csup\u003e29\u003c/sup\u003e. These tensions are compounded by the different demands placed on knowledge by various actors: policymakers require scalable, evidence-based metrics for systemic change, NGOs often advocate for urgent action based on ethical imperatives, and agribusinesses must balance sustainability with profitability. Navigating these sometimes-conflicting imperatives requires a co-production approach to knowledge, wherein farmers, researchers, NGOs, policymakers, and corporate actors collaboratively engage in research design, implementation, and evaluation\u003csup\u003e25\u003c/sup\u003e, thereby increasing knowledge credibility, saliency and legitimacy\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, there are significant gaps in the regenerative agriculture knowledge landscape. Economic viability remains underrepresented in most regenerative agriculture literature and discourses\u003csup\u003e13,40\u003c/sup\u003e. With notable exceptions\u003csup\u003e5\u003c/sup\u003e, most case studies on regenerative agriculture focus on ecological outcomes, while fewer examine the profitability, labour implications, and market access realities that ultimately determine whether farmers can adopt and sustain regenerative practices. Without clear, localized data on financial sustainability, the transition to regenerative agriculture may remain accessible only to well-resourced or ideologically driven farmers. Policy integration is another critical barrier. Although regenerative agriculture aligns well with long-term sustainability goals, it often exists outside current policy and subsidy frameworks, particularly within the EU\u0026apos;s Common Agricultural Policy (CAP). This lack of institutional alignment creates disincentives, especially for conventional farmers who might be open to transitioning but lack the necessary guidance or economic support.\u003c/p\u003e\n\u003cp\u003e3.4 Toward a more effective and inclusive knowledge ecosystem\u003c/p\u003e\n\u003cp\u003eTo strengthen the role of knowledge networks in the adoption and scaling of regenerative agriculture, a multifaceted approach is required. First, supporting hybrid knowledge spaces is recommended: promoting co-creation of knowledge that integrates scientific rigor with farmer experience can enhance both credibility and practical relevance. Second, strengthening multi-scale connectivity is needed to build linkages across local, national, and international networks, thereby ensuring that global strategies remain grounded in local realities. Intermediaries such as NGOs and extension services play a crucial role at facilitating these exchanges. Third, digital platforms should be leveraged responsibly, with careful curation and management to promote the dissemination of balanced, evidence-informed, and context-sensitive information. Establishing mechanisms for content verification and transparent sourcing can help mitigate the risks of misinformation. In conclusion, while knowledge networks have immense potential to drive the transformation toward regenerative agriculture, realizing this potential depends on how these networks are structured, governed, and made accessible. Addressing existing challenges and leveraging digital and institutional tools thoughtfully can foster more resilient, inclusive, and impactful knowledge ecosystems for a regenerative future.\u003c/p\u003e"},{"header":"4.\tMethods","content":"\u003cp\u003eTo explore the knowledge diffusion dynamics that guide the implemented practices of self-identified regenerative farmers we focused on five case-countries across Europe: Germany, the Netherlands, France, Spain, and Portugal. These countries were selected because they feature a diverse range of farming systems in Southern and Western Europe, with contrasting environmental challenges, socio-economic conditions and political contexts\u003csup\u003e41,42\u003c/sup\u003e. Self-identified regenerative farmers were contacted and those who agreed were interviewed (Fig. 5). Their responses were structured into a database following a node and edge list format to perform SNA. The following sections provide a detailed overview of each methodological step.\u003c/p\u003e\n\u003cp\u003e4.1 Data collection\u003c/p\u003e\n\u003cp\u003eTo identify regenerative farmers, we utilized the Regenerative Actor Database compiled in 2024 by Schreefel et al.\u003csup\u003e13\u003c/sup\u003e, which includes all food system actors (n=849) with a demonstrated commitment to regenerative agriculture in the case-study countries. Of these, 281 self-identified regenerative farmers were identified in the database and contacted via email, phone, and social media to participate in the study. A total of 128 farmers agreed to participate, resulting in a response rate of 46% (Supplementary Table 7). The study included 23 farmers from Germany, 30 from the Netherlands, 18 from France, 46 from Spain, and 11 from Portugal.\u003c/p\u003e\n\u003cp\u003eData collection was conducted through semi-structured interviews between April and November 2024, with an average interview duration of one hour. Interviews followed a semi-structured protocol based on a two-section questionnaire (see Supplementary Questionnaire for details) and were conducted concurrently across the five case-study countries by co-authors fluent in the respective local languages. The interviews collected data in four key areas. First, farmers provided descriptive information about themselves and their farms via Microsoft Forms, including details such as years of farming experience, years practicing regenerative agriculture, farm type, and farm size. Farm sizes were benchmarked on a five-point scale from very small to very large against national inventories (Supplementary Table 8). Second, using Microsoft Excel, farmers listed the regenerative practices implemented on their farms (e.g., cover cropping, no-tillage). Third, they identified the sources of knowledge that informed each practice and specified the role (e.g., advisory services, other farmers) of each source. Fourth, farmers rated both the frequency in which they received knowledge and their level of trust in these sources.\u003c/p\u003e\n\u003cp\u003eThe frequency of knowledge received was assessed on a four-point scale: 1) rare - at most once per year; 2) occasional - at least twice per year but less than once per month; 3) frequent - at least once per month but less than once per week; 4) very frequent \u0026ndash; at least once per week. Farmers also evaluated the trustworthiness of knowledge sources on a five-point trust scale: 1) strong distrust - deep mistrust of the source; 2) moderate distrust - some reservations about credibility; 3) neutral - neither trust nor distrust; 4) moderate trust - considerable confidence in reliability; 5) strong trust - high confidence in accuracy. However, as trust ratings were predominantly high, only frequency of knowledge received was retained for SNA (see Supplementary Table 5).\u003c/p\u003e\n\u003cp\u003e4.2 Social Network Analysis\u003c/p\u003e\n\u003cp\u003eSNA was employed to analyse knowledge diffusion among the responding regenerative farmers. This approach considered recurring patterns of diffusion as social networks, wherein actors were represented as \u0026quot;nodes\u0026quot; in the network (Fig. 1), and the knowledge flow between respondent farmers and knowledge sources was depicted as \u0026quot;edges\u0026quot;\u003csup\u003e43,44\u003c/sup\u003e. Given the nature of farmers\u0026apos; networks, which generally involve numerous actors\u003csup\u003e18,45\u003c/sup\u003e, this study focused on egocentric, or farmer-centric, networks of respondent farmers to identify key sources of knowledge on specific regenerative practices. Consequently, only first-degree connections were considered, meaning that only edges between the respondent farmers (egos) and their knowledge sources (alters) were included.\u003c/p\u003e\n\u003cp\u003eThe data collected during the interviews were systematized into a node catalogue, detailing each actor\u0026rsquo;s name and node attributes, along with an edge list encompassing all ego - alter connections and their respective edge attributes. Networks were analysed and visualized using Gephi \u0026copy; 10 software\u003csup\u003e46\u003c/sup\u003e. Network graphs were generated to display nodes and their edges (Fig. 5), with directional edges indicated by arrows from the knowledge receiver node (respondent farmer) to the knowledge source node (source actor)\u003csup\u003e45\u003c/sup\u003e. Nodes were sized according to actors\u0026rsquo; in-degree and coloured based on actors\u0026rsquo; roles\u003csup\u003e18,47\u003c/sup\u003e. The actor roles were categorized into 10 groups: research or education; farmer(s); author; advisory; online content and media; respondent farmer; NGOs; value chain business; government agency, and other actors (see Supplementary Table 9 for category explanations). Edge thickness represented the strength of knowledge diffusion, corresponding to the frequency of received knowledge on a four-point frequency scale (i.e., rare to very frequent).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferent edge colours indicated the practices being mentioned. As the practices cited by farmers exhibited varying degrees of overlap and specificity, they were aggregated into 52 clusters based on the methodology of Schreefel et al.\u003csup\u003e13\u003c/sup\u003e. For instance, some farmers referred to minimal tillage, others mentioned reduced ploughing, and some described specific tillage types (e.g., disk ploughing), depths, or frequencies. These variations were all clustered under minimal tillage (see Supplementary Table 10). Of these clustered practices, only the eight most frequently mentioned were used for visualization, while the remaining practices were grouped under the category \u0026quot;other practices\u0026quot;. Additionally, a tenth category, \u0026quot;multiple practices\u0026quot;, was introduced to group all edges involving exchanges on more than one of the nine primary clustered practices. For example, if a respondent farmer reported exchanging knowledge on \u0026ldquo;cover crops\u0026rdquo; and \u0026ldquo;crop diversity\u0026rdquo; with the same source, the parallel edges between the two nodes were merged under a single edge labelled \u0026quot;multiple practices\u0026quot;, with their weights averaged. Duplicated edges were similarly merged with averaged weights.\u003c/p\u003e\n\u003cp\u003eNetwork visualizations were constructed at two levels: 1) including all actors and edges, and 2) including only actors with an in-degree above two, with respondent farmers clustered by country of origin (i.e., Germany, the Netherlands, France, Spain, and Portugal). Gephi\u0026rsquo;s built-in metrics were used to compute both node and network level characteristics, allowing for the identification of knowledge diffusion and central actors providing knowledge to regenerative farmers. Node-level metrics included out-degree (number of edges emanating from a node, representing the number of sources cited by a farmer), in-degree (number of edges leading to a node, representing the number of times an actor was cited as a source of knowledge), and betweenness centrality (extent to which a node lays between other nodes)\u003csup\u003e45,48\u003c/sup\u003e. Further knowledge on these metrics and how these are quantified can be found in Supplementary Table 11. Key actors in knowledge diffusion were identified based on in-degree and betweenness centrality\u003csup\u003e18,47\u003c/sup\u003e. Actors with high in-degree were considered central to knowledge diffusion as they provided knowledge to many other actors\u003csup\u003e49\u003c/sup\u003e, while actors with high betweenness centrality were deemed crucial in controlling knowledge flow, acting as brokers or gatekeepers connecting otherwise disconnected actors\u003csup\u003e47\u003c/sup\u003e. Farmers\u0026rsquo; utilization of knowledge sources was assessed based on out-degree, where a high out-degree indicated that farmers used a greater variety of knowledge sources\u003csup\u003e45\u003c/sup\u003e. At network level, in-degree centralization was computed to assess the extent to which knowledge diffusion was concentrated on a few dominant sources\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe processed datasets generated and analysed during the current study are available in the supplementary materials. Datasets containing privacy-sensitive information are not publicly available but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis study was funded by The Nest Family Office (TNFO). The funder played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript.\u003c/p\u003e\n\u003cp\u003eAuthorship\u003c/p\u003e\n\u003cp\u003eLS: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eES: Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization.\u003c/p\u003e\n\u003cp\u003eRB: Validation, Formal analysis, Investigation, Data Curation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eFA: Validation, Formal analysis, Investigation, Data Curation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eSF: Validation, Formal analysis, Investigation, Data Curation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eJG: Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003ePT: Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eNAG: Methodology, Investigation, Supervision, Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interest\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCampbell, B. 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Y., Karlina, N., Munajat, M. D. E. \u0026amp; Ningrum, S. Mapping Policy Actors Using Social Network Analysis on Integrated Urban Farming Program in Bandung City. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, (2023).\u003c/li\u003e\n \u003cli\u003eTeodoro, J. D., Baird, J. \u0026amp; Otung, I. Longitudinal Network Analysis on a Farmers\u0026rsquo; Community of Practice and Their Changes in Agricultural Systems Management. \u003cem\u003eSoc Nat Resour\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 90\u0026ndash;107 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Regenerative farming, social network analysis, social media, food production, sustainable development","lastPublishedDoi":"10.21203/rs.3.rs-7519599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7519599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Agriculture supports human well-being but exerts pressure on the environment, driving climate change and biodiversity loss. Regenerative agriculture is gaining traction as a solution, yet how farmers access knowledge about regenerative practices remains unclear. This study interviewed 128 self-identified regenerative farmers across five European countries, identifying 834 unique knowledge sources spanning over 52 distinct practices. Using Social Network Analysis, we show that both national and international knowledge sources play pivotal roles. Peer-to-peer learning dominated nationally, while prominent international figures (e.g. Richard Perkins and Allan Savory) shaped broader narratives across borders through diverse channels, including books and digital platforms. This suggests international knowledge sources tend to inspire guiding principles, while local actors adapt practices to context. Our findings highlight the importance of hybrid knowledge spaces that connect scientific and farmer expertise, strengthen multi-scale networks through intermediaries, and ensure digital platforms offer evidence-based, context-informed content to scale regenerative agriculture effectively.","manuscriptTitle":"Global and local knowledge networks spread regenerative agriculture in Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 08:15:06","doi":"10.21203/rs.3.rs-7519599/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d13bfac3-c0d5-4dcb-a798-8f81b31b2e37","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54082867,"name":"Earth and environmental sciences/Ecology/Agroecology"},{"id":54082868,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"}],"tags":[],"updatedAt":"2025-10-21T10:31:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 08:15:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7519599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7519599","identity":"rs-7519599","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}