Ex ante Assessment of the Sustainability of Alternative Cropping Systems: Implications for Using Multi-criteria Decision-Aid Methods - A Review
Bibliographic record
Abstract
Sustainability is a holistic and complex multi-dimensional concept encompassing economic, social and environmental issues, and its assessment is a key step in the implementation of sustainable agricultural systems. Realistic assessments of sustainability require: (1) the integration of diverse information concerning economic, social and environmental objectives; and (2) the handling of conflicting aspects of these objectives as a function of the views and opinions of the individuals involved in the assessment process. The assessment of sustainability is therefore increasingly regarded as a typical decision-making problem that could be handled by multi-criteria decision-aid (MCDA) methods. However, the number and variability of MCDA methods are continually increasing, and these methods are not all equally relevant for sustainability assessment. The demands for such approaches are also rapidly changing, and faster ex ante assessment approaches are required, to address scales currently insufficiently dealt with, such as cropping system level. Researchers regularly carry out comparative analyses of MCDA methods and propose guidelines for the selection of a priori relevant methods for the assessment problem considered. However, many of the selection criteria used are based on technical/operational assumptions that have little to do with the specificities of ex ante sustainability assessment of alternative cropping systems. We attempt here to provide a reasoned comparative review of the main groups of MCDA methods, based on considerations related to those specificities. The following main guidelines emerge from our discussion of these methods: (1) decision rule-based and outranking qualitative MCDA methods should be preferred; (2) different MCDA tools should be used simultaneously, making it possible to evaluate and compare the results obtained; and (3) a relevantly structured group of decision-makers should be established for the selection of tool variants of the choosen MCDA methods, the design/choice of sustainability criteria, and the analysis and interpretation of the evaluation results.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".