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Record W4410962696 · doi:10.3897/aca.8.e157080

The role of standardised observation in preventing biosphere risk: Unlocking agrifood systems transformations in LTSER Platforms and beyond

2025· article· en· W4410962696 on OpenAlexaff
Pablo F. Méndez, Mihai Adamescu, Bastian Bertsch-Hoermann, Floriane Clément, M. Cohen, Ricardo Dı́az-Delgado, Jan Dick, Marta Dobrovodská, David Fajardo-Ortiz, Sabrina Gaba, Veronika Gaube, L. Halada, Jennifer M. Holzer, Zita Izakovičová, Daniel E. Orenstein, Elisa Oteros‐Rozas, Giovanna Ottaviani-Aalmo, Guillermo Palau-Salvador, Inês T. Rosário, Marta G. Rivera‐Ferre, Margarida Santos‐Reis, Jana Spurelova, Paolo Vezza, Sergio Villamayor‐Tomás, Tim Williams

Bibliographic record

VenueARPHA Conference Abstracts · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsBrock University
FundersEuropean Commission
KeywordsBiosphereBusinessEnvironmental resource managementEnvironmental planningRisk analysis (engineering)GeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Humans intensifying interactions with natural systems due to population growth and escalating energy, land and resources use, are accelerating a deep erosion of ecological functions and values upon which socioeconomic systems and human wellbeing depend (Rockström et al. 2023). This erosion is compounded by an ever tighter global interconnection of crises spanning multiple sociopolitical, technological and environmental domains, in what has been termed a polycrisis (Søgaard Jørgensen et al. 2023). The latter encompasses the nexus among human health, biodiversity, climate change, water availability and quality, food (in)security, and inequalities (IPBES 2024). A burgeoning field of research, the nexus polycrisis posits a rather existential question: are humans locked-in in an undesirable trajectory increasingly difficult to escape from? But more importantly, how can humans leverage their collective-action potential to prevent systemic risks and shift to more sustainable pathways at local and global scales? Indeed, the widespread policy consensus is no longer why or whether sustainability pathways are required, but how to trigger, operationalize and accelerate them with urgency and justice (UNEP 2021, UNEP 2021,IPCC 2023 ,IPBES 2024). In this presentation, we describe the methodological approach and preliminary results of a research initiative dealing with sustainability pathways in the European agrifood sector. The agrifood sector is exemplary of locked-in undesirable trajectories, with implications for the nexus polycrisis and across key production-consumption value chains (Lamine and Marsden 2023). Historically, agrifood technological innovation and high specialization have come at the cost of environmental degradation at multiple spatial scales, ultimately compromising the sustainability of land productivity and food production, and hence human wellbeing (IPBES 2024). This is likely due to a long-term process of post-war stabilization through the convergence of innovations, and technocratic strategies and policies at different levels of the agrifood system (e.g., production, processing, consumption), which generated the system’s “resistance to change” and lock-in at present (Conti et al. 2021, Williams et al. 2023). Using several cases of agrifood systems in 8 LTSER Platforms, our research focuses on two interrelated knowledge and data gaps. First, governance and politics are inherently implicated in any effort to foster sustainability pathways (Méndez et al. 2023). However, understanding the role of governance in fostering sustainability pathways remain underdeveloped in the literature, in particular around the more specific causal mechanisms underlying undesirable trajectories that become stuck in costly-to-escape social-ecological traps, in particular locked-in states (Lambert-Peck et al. 2024). Thus, we engage methodologically with the effect and feedback dynamics of different governance structures on locked-in social-ecological states. Second, policy mixes for tackling systemic lock-ins and transformative changes require a strong coordination to achieve the necessary directionality and coherence across policy areas affecting multiple socio-economic interests, environmental domains and cultural value systems. In turn, evidence-based coordination mechanisms and policy mixes require long-term research and monitoring programs based on harmonised sets of variables, in order to achieve meaningful understanding of policy impact over time (Pacheco-Romero et al. 2020). Here, we contribute to develop representative, efficient and feasible European long-term monitoring programs, with a focus on eLTER RI Standardised Observation system. Our particular focus is on the development of eLTER RI’s approach and protocol for the long-term monitoring of social-ecological governance in LTSER platforms, in coordination with other key protocols such as those focused on ecosystem services and land-use change. The role of standardised biophysical data and variables is also explored. We aim at revealing their potential to understand the role of key processes like ecological suppression in producing system-level social-ecological lock-ins that can propagate and cumulate across scales and induce systemic risks at biosphere level. We hope that this research may inform the study of social-ecological lock-ins in a more comparative and applicable way beyond agrifood systems and LTSER Platforms. We also hope that it can support transdisciplinary research processes in their efforts to explore and support co-produced solutions for collectively learning how to navigate away from and out undesirable trajectories of development in a safe and equitable way. At policy level, we aim at highlighting the potential of eLTER RI for informing multi-level policies and governance modes in their role to prevent local and global situations prone to systemic risks derived from lock-in phenomena (e.g., through the development of early warning systems to detect signs of lock-in at multiple scales). Finally, we show how this research is informing the foundation for a collaborative space aimed at mutual-learning and knowledge exchange between the LTSER Platforms network and the nascent network of Soil Health Living Labs under the Soil Mission.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.221
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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