Impacts of COVID-19 on agriculture and food systems in Pacific Island countries (PICs): Evidence from communities in Fiji and Solomon Islands
Bibliographic record
Abstract
CONTEXT: COVID-19 mitigation measures including border lockdowns, social distancing, de-urbanization and restricted movements have been enforced to reduce the risks of COVID-19 arriving and spreading across PICs. To reduce the negative impacts of COVID-19 mitigation measures, governments have put in place a number of interventions to sustain food and income security. Both mitigation measures and interventions have had a number of impacts on agricultural production, food systems and dietary diversity at the national and household levels. OBJECTIVE: Our paper conducted an exploratory analysis of immediate impacts of both COVID-19 mitigation measures and interventions on households and communities in PICs. Our aim is to better understand the implications of COVID-19 for PICs and identify knowledge gaps requiring further research and policy attention. METHODS: To understand the impacts of COVID-19 mitigation measures and interventions on food systems and diets in PICs, 13 communities were studied in Fiji and Solomon Islands in July-August 2020. In these communities, 46 focus group discussions were carried out and 425 households were interviewed. Insights were also derived from a series of online discussion sessions with local experts of Pacific Island food and agricultural systems in August and September 2020. To complement these discussions, an online search was conducted for available literature. RESULTS AND CONCLUSIONS: -emergence of cultural safety networks and values, such as barter systems. Households in rural and urban communities appear to have responded positively to COVID-19 by increasing food production from home gardens, particularly root crops, vegetables and fruits. However, the limited diversity of agricultural production and decreased household incomes are reducing the already low dietary diversity score that existed pre-COVID-19 for households. SIGNIFICANCE: These findings have a number of implications for future policy and practice. Future interventions would benefit from being more inclusive of diverse partners, focusing on strengthening cultural and communal values, and taking a systemic and long-term perspective. COVID-19 has provided an opportunity to strengthen traditional food systems and re-evaluate, re-imagine and re-localize agricultural production strategies and approaches in PICs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".