Cultivating climate resilience: a participatory assessment of organic and conventional rice systems in the Philippines
Bibliographic record
Abstract
Abstract Climate change poses serious threats to agriculture. As a primary staple crop and major contributor to agriculturally derived greenhouse gas (GHG) emissions, rice systems are of particular significance to building climate resilience. We report on a participatory assessment of climate resilience in organic and conventional rice systems located in four neighboring villages in Negros Occidental, Philippines. The Philippines is one of the foremost countries impacted by climate change, with an increasing incidence of climate-related disturbances and extensive coastlines, high population density and heavy dependence on agriculture. Using the United Nations Food and Agriculture Organization's Self-evaluation and Holistic Assessment of climate Resilience of farmers and Pastoralists (SHARP) tool, we measured 13 agroecosystem indicators of climate resilience, and assessed the degree to which household, farm, and community mechanisms and outcomes impact adaptation capacity, mitigation potential and vulnerability. We used a participatory approach to situate these indicators in their socio-ecological context, and identify targeted interventions for enhancing climate resilience based on local farmer experiences and socio-ecological conditions. Comparison of climate resilience indicators across organic and conventional rice systems in this region indicated that organic rice systems are more climate resilient than their conventional counterparts. As such, increased policy support for the development of organic rice systems are critically important as an adaptive mechanism to augment food security, mitigate GHG emissions and improve climate resilience in the Philippines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".