Giving regenerative agriculture an agronomic perspective: a proposed framework from the food and beverage industry
Bibliographic record
Abstract
Food systems face significant challenges that include increasing demand for agricultural products and accelerating environmental degradation. Regenerative agriculture has emerged as concept to reduce environmental harm while maintaining or even improving productivity. However, adoption of regenerative agriculture remains limited. This partly due to the absence of a shared definition and a standardised process to monitor, assess and report regenerative agriculture outcomes. To address this gap, SAI Platform, a member-led organisation within the food and beverage sector, collaborated with stakeholders to develop a global framework for regenerative agriculture. Drawing from a review of existing frameworks and consultations with SAI Platform members, farmers, and academics, we developed a framework that defines regenerative agriculture as an outcome-based approach that enhances environmental impact. It includes eight regenerative agriculture outcomes across the environmental areas of soil health, biodiversity, water and climate and suggests indicators to quantify progress. The framework process emphasises the need to understand local contexts and farmer needs when implementing regenerative agriculture. It does so through a four-step process that includes (i) a context analysis to identify key material criteria of a production system, (ii) the prioritisation of outcomes based on the context analysis, (iii) the selection of practices to achieve improved performance against prioritised outcomes, and (iv) the development and implementation of continuous improvement plans to monitor and report progress. Farm groups or individual farms can use this framework to independently verify the implementation of the steps defined in the framework and claim different performance levels for progress towards regenerative agriculture. These claims create a foundation for regenerative agriculture programmes, incentive mechanisms, and corporate reporting. While the framework is a starting point, collaboration and refinement are necessary to address evolving challenges in implementation. SAI Platform commits to research and stakeholder engagement to continuously improve the framework and support fair transitions towards regenerative agriculture.
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 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 | 0.025 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".