Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency
Bibliographic record
Abstract
With the rising oil prices, climate change, and the ever increasing burden of nutrition-related disease, food security is of growing research interest in academic disciplines spanning agronomy to epidemiology to urban planning. Some governments have developed progressive policies encouraging individuals to consume locally produced foods in order to support local economies, improve agricultural sustainability and community access to food, and to plan and prepare for adverse environmental impacts on food security. However, fundamental methods are lacking for conducting research on food security across these various disciplines. In this article, we first present a method to measure agricultural self-sufficiency, which we refer to as our self-sufficiency index (SSI) for the province of British Columbia, Canada. We then present a Bayesian autoregressive framework utilizing readily available agricultural data to develop predictive smoothing models for the SSI. We find that regional capital investment in agriculture and cropland acreage is the strong predictor of SSI. To accommodate spatial variability, we compare linear regression models with spatially correlated errors to less traditional spatially varying coefficient models, and find that the former class results in better model fit. The smoothed maps suggest that relatively strong self-sufficiency exists only in subset clusters in the Okanagan, Peace River, and lower mainland regions. In spite of policy to promote local food, the existing local agricultural system is insufficient to support a large-scale shift to local diets. Our approach to estimating neighborhood-based self-sufficiency with a predictive model can be extended for use in other regions where limited data are available to directly assess local agriculture and benefit from explicit consideration of spatial structure in the local food system.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".