Bridging two communities in farming system research: IFSA Europe Group and Farming System Design (slideshow)
Bibliographic record
Abstract
Current transitions in agri-food systems are marked by multi-level complexity and uncertainty. These transitions are shaped by ecological, economic, and social dynamics under extremes of climate change, market volatility, and geopolitical instability. In this context, two major research communities offer complementary insights, albeit insufficiently connected. On the one hand, the International Farming Systems Association IFSA (Europe Groupe) emphasizes social science perspectives on farmers’ behavior, cultural determinants, and innovation mechanisms. On the other hand, the Farming System Design (FSD) focuses on theory-driven modeling, design approaches, and agronomic systems. Both communities face common challenges in transitioning to sustainable agriculture, but landscape-level integration and modeling social and behavioral aspects of farming systems are still underexplored. This presentation examines how integrating these perspectives can generate more systemic and practice-relevant knowledge for sustainable farming systems. The presentation highlights opportunities to integrate empirical and interdisciplinary approaches with exploratory modeling, expand design frameworks to include social-behavioral dimensions and path dependencies, and strengthen landscape-level analyses. Practical propositions that emerge from these collaborations include: (1) teaching to integrate holistic and design-based approaches into interdisciplinary curricula, (2) advisory services to enhance farmer engagement through co-design and visualization tools, and (3) R&D institutions to mutualize connections with learned societies and policymakers to promote actionable systemic innovation. In conclusion, this presentation emphasizes the importance of cross-community collaboration in supporting transformative research that addresses three areas: (1) landscape-community dynamics, (2) capacity development for systemic change, and (3) the food production-consumption nexus, within a broader framework of sustainability challenges in agricultural systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".