How Do Small-Scale Cassava Producers Overcome Global Issues? Cassava Profit and Technical Efficiency in Cambodia
Bibliographic record
Abstract
Cassava producers face numerous economic and natural challenges that impact their profitability. Economically, they encounter price fluctuations for cassava chips and fresh tubers in the global market. Additionally, unexpected weather conditions and diseases affect production. Given the volatility of global prices and unpredictable natural events, producers employ various strategies to maximize their diminishing profits. However, it remains uncertain which practices are more effective in achieving profitability. The factors that influence profitability in farming, such as density, replanting, and the choice of selling the product, either fresh or dry, have been identified in this study. Therefore, the objective of this study is to investigate the determinant factors, including inputs to profit efficiency and farming strategies specific to cassava plantations, that lead to enhanced profit capture. We employ a Cobb-Douglas stochastic frontier model to analyze the technical efficiency of profit capture. Our study suggests that producers should avoid buying additional bunches for replanting and focus on planting at an optimized density to maximize profits. Other strategies showed uncertain outcomes. Knowledge of correct farming practices can improve efficiency and profit optimization.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".