The contribution of multi-purpose farming to the food security of small-scale farmers: An agro-economic analysis in the lowland Mekong alluvial plain
Bibliographic record
Abstract
Insights from Cambodia's rural communities Section BThe contribution of multi-purpose farming to the food security of small-scale farmers: An agro-economic analysis in the lowland Mekong alluvial plain CHAPTER 4Agricultural development models are the focus of intense debate in the Cambodian policy-making arenas.A model inspired by the 'Green Revolution', which promotes industrialization of rice cropping systems and is mainly dependent on external inputs, is usually contrasted with multi-purpose farming (MPF) in which rice cropping is integrated with other production to maximize their interactions and complementarities.The purpose of this research is to present some economic arguments based on the experience in promoting MPF of CEDAC (Centre d'Etude et de Dévelopement Agricole Cambodgien, also known as the Cambodian Center for Study and Development in Agriculture).We develop a comparative analysis between conventional rice cropping systems and MPF to analyze the production economics and the overall patterns of household labor diversification.Focusing primarily on rainy-season rice production, we attribute significant advantage to MPF against non-MPF practices.These differences are perceptible in higher rice yield, lower cash-paid costs, and higher value-added per hectare.However, these differences become less significant when multi-purpose farming is only partially implemented, i.e. if some elements of the system are missing.MPF provides employment opportunities that represent a reliable alternative to job migration.Under MPF, family labor is used more on-farm than is the case with non-MPF farms and is more homogenously distributed throughout the year.However, we identify barriers that curb the scaling-up of this innovation, which include limited access to information, anticipated lack of labor, lack of capacity or technical skills, lack of up-front capital to make the initial investment in land conversion to MPF, and land plot sizes that are too small to be converted to this model.We also discuss the opportunities to create markets for products of differentiated quality produced under multi-purpose farming.
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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 | 0.000 | 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) | 0.002 | 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".