Multidimensional Benefits of Sustainable Agriculture Practices of Cambodian Smallholder Farmers
Bibliographic record
Abstract
Agriculture is an important mainstay of the Cambodian economy and recent agriculture developments have lifted many people out of poverty. However, some key challenges remain in Cambodian rural areas. To further sustain the country’s development, new ways need to be found to drive future growth without negatively impacting its existing resources. Recent efforts led by development stakeholders have been dedicated to enhanc the sustainability of Cambodian agriculture. The local non-governmental organization MODE jointly with Louvain Cooperation focuses on helping vulnerable farmers to transition towards sustainable agriculture practices through specific training. This paper aims to assess the benefits of such an approach by using SAFA (Sustainability Assessment of Food and Agriculture systems), a methodology developed by the Food and Agriculture Organization. Core indicators of the four pillars of sustainability were evaluated through interviews with eighty farmers, equally distributed in two groups (target farmers and a control group), to assess the agricultural sustainability of their local farming system. Statistical analysis revealed significant differences between the two groups. The target group which was supported in their transition to sustainable practices showed a significant increase of net incomes, more diverse food production, and higher number of actions already taken or planned in order to mitigate the inherent risks of food production. The global index resulting from the combination of all core indicators revealed a significant difference between the groups, with more sustainable practices for the target farmers. However, overall the level of sustainability remained low to very low in both groups, which was partly due to the choice to work with vulnerable people defined by low productivity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".