Assessing sustainability in agricultural landscapes: a review of approaches<sup>1,2</sup>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research and development agencies, as well as policy makers and agri-food enterprises, need reliable data to support informed decisions that can improve the sustainability of agricultural landscapes. We present a review of agricultural sustainability assessment frameworks (ASAF) that identifies the features most relevant to monitoring progress towards sustainability goals for agricultural landscapes. This qualitative review considers a variety of approaches for defining goals and for selecting stakeholders, spatial and temporal boundaries, indicators, and analytical approaches. We focused on assessment frameworks that (i) include environmental, social, and economic implications of agriculture; (ii) are applicable to multiple, non-specified farm system types; (iii) are described in an English language, peer-reviewed publication; (iv) have been developed for use at a farm system to regional spatial scale; (v) engage stakeholders; (vi) provide case studies; and (vii) could be used in a variety of contexts across the globe. Based on the review, we provide recommendations for further development and use of assessment frameworks to better address the needs of agricultural research, extension, and development organizations. We recommend an agro-ecosystem approach to help stakeholders identify appropriate indicators for their situation. Assessment methods need to be flexible enough for adaptation to a spectrum of agricultural landscapes and changing environmental conditions, and remain relevant as farmers and other stakeholders acquire new information, resources, and different management techniques. We find that to address information gaps across different scales from farm to region will require creativity and some reliance on local knowledge systems to support adaptive management. Assessment results should communicate relationships among ecosystem services and socio-economic activities affected by agricultural landscapes. Visualization tools can facilitate understanding of trade-offs and synergies among sustainability goals as reflected by individual indicators.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it