Practice of Biodiversity conservation and Agroecology Enhance Climate Change Resilience of Organized Small Scale Organic Farmers in the Philippines \n
Bibliographic record
Abstract
Conservation and utilization of rice varieties by small scale farmers in the Philippines for more than two decades has led to retrieval of more than a thousand varieties of rice. The variety of rice plant characters like performance under organic farming, growth duration, height, differential resistance to pests and diseases, adaptation to climate change as well as eating quality has challenged farmers to learn breeding from their scientist partners. As a result of breeding, more than a thousand improved rice varieties were developed plus about 500 breeds developed by farmers themselves. Farmers maintain the rice varieties in trial farms which contain at least 50 varieties. MASIPAG has developed a conservation support system through a national backup farm, regional back up farm, and to some extent, provincial back up farm. \n \nFarmers and their organizations are indispensable in the development and practice of agroecology to challenge the dominant agricultural paradigm of ‘modern’, chemical and GMO farming. Small scale farmers who were disillusioned with chemical farming are now converting into organic and agroecological technologies. MASIPAG farmer were trained to develop skills of observation, trial and erro,r and establishing cause-and-effect, some of the rudiments of science. Consequently, many farmers have developed and adapted technologies on seeds, agronomic, soil and nutrient management, alternative pest management, storage, processing and marketing. Organic farming is then more sustainable with nutrient integration, and cultivation of below and above ground biodiversity with a resulting ecosystem of enhanced biological interactions and synergism. \n \nFarmer-to-farmer diffusion of seed and agroecology approaches has made organic farming diffuse in Farmers’ Organizations (PO/FO) and very cost effective because the users of technologies are right at where the technologies are developed. The technologies developed are what the farmers need. Due to common language, farmers understand more the other farmers compared to technical extension workers who often have different perspectives and priorities. The organized farming community enhances self confidence of other farmers in converting into organic and agroecological approaches based on the actual experiences and testimonies of other farmers. \n \nThe use of agrobiodiversity makes seeds in the hands of the small scale farmers resulting to less external cost in production and the differential responses of varieties, crops and livestocks to climate change is a built-in insurance and reduce exposure to losses. Agroecology methods make organic farming more sustainable. Both of the above contribute to greater capacity for resilience of farmers in the face of climate change. \n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".