Multi-criteria decision analysis comparing agricultural production methods : protocol for analyzing British Columbia blueberries
Bibliographic record
Abstract
INTRODUCTION: Methods such as Multi-criteria decision analysis (MCDA) are often applied to assess how preferences to make rational choices are applied. This thesis aims to examine how farmers balance environmental and social factors of sustainability and health with economic factors (e.g. costs) by assessing their preference for applying alternative agricultural approaches (e.g. conventional, agro-ecological/organic, and integrated farming/mixed-methods). METHODS: First, a systematic bibliometric review of studies that used MCDA techniques for agricultural purposes was conducted to consider the ways that the analytical approach was being applied in this area. The review was restricted to all English language studies of farm-based agricultural studies that considered cost in their analysis. Studies from the Web of Science, CAB Direct, and Agriculture & Environmental Science databases were reviewed to identify publication trends that helped situate the objectives the thesis’ own MCDA feasibility study. Second, a small group (9) of BC Blueberry farmers were interviewed using an Analytic Hierarchy Process (AHP) MCDA technique to elicit their preferred production system while considering potential constraints. The costs of agricultural production systems were divided by the aggregate value scores of the AHP, and systems ranked on their cost-benefit ratio. RESULTS: MCDAs in agriculture have become increasingly popular over time, particularly AHPs in Europe and Asia, and in fruit, vegetable, and nuts farming sectors. Most studies considered costs as one of the criteria in the analysis, most often as a production/operating cost. Health was not mentioned extensively in these studies. The MCDA study showed that organic farming is the most preferred method without the consideration of costs, but conventional farming was the most preferred in the cost-benefit ratio. CONCLUSION: Farmers prefer to be more mixed-methods or ecological (without the consideration of costs), constraints (specifically costs) prevent them from practicing their preferences. As a novel approach in agriculture, the MCDA-CBA is a feasible tool to understand farmer preferences and how they can be advocated for to achieve more sustainable and healthy processes in policy. MCDA-CBA has potential for understanding health and sustainability as connected with similar, if not the same, goals and criteria.
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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 | 0.042 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.009 |
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".