Resilience of Food Farming in Rapidly Urbanizing Regions
Bibliographic record
Abstract
Assessing the resilience of farm-level agroecosystems offers a way to inform the allocation of scarce resources needed to sustain local food production in rapidly urbanizing regions. Clark County, Washington, is an understudied part of the Portland–Vancouver metropolitan region, with sprawling development, fragmentation, changing farmer demographics, and a diversity of farm types. This case study sought to answer the following questions: 1) What are current and potential vulnerabilities for urban area food farms? 2) What will be needed to retain and enhance local food production capacity for the long term? and 3) What are useful indicators of environmental, economic, and social resilience for food-producing farms in rapidly urbanizing regions such as Clark County? A resilience theoretical framework and principles of agroecology guided design, data gathering, and analyses. Secondary data informed both the county-level and farm-level analyses. Compiled from several sources, a list of 100 farms was used to select a diversity of farms direct marketing fruits, vegetables, and/or nuts. Primary data collection included: semi-structured interviews and farming system assessments on 23 farms; two farmer roundtables; and participant observation in a broad spectrum of agriculture-focused activities. A farm resilience assessment framework comprising 29 indicators across agronomic, economic, environmental, and social realms was developed to gather, quantify, and analyze data from the study farms. Study farms were found to implement a diversity of innovative agroecological and marketing strategies to help overcome risks. Scores were highest for innovative farms producing a diversity of products for a diversity of markets while protecting the environment. While the literature suggests that diversity and direct marketing improve farm resilience and foster a sustainable local food movement, these results show that such characteristics are insufficient in themselves. Despite performing well by these criteria, 11 of the study farms no longer produce food commercially. Secondary data revealed a 16% reduction in cropland acres in the County (2012—2017). Over 6,000 acres of productive land was converted to urban and/or suburban development (2001—2016). To protect remaining agricultural capacity, this study found an urgent need to reshape local policies, public institutions, and support networks in accordance with stated farmer needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".