Determinants of Chickpea (Cicer arietinum L.) Marketed Surplus Among Smallholder Farmers in Humbo and Damot Gale Woredas, Southern Ethiopia
Bibliographic record
Abstract
This research was aimed to study the chickpea (Cicer arietinum L.) marketed surplus among smallholder farmers in the Humbo and Damot Gale Woredas.A multi-stage sampling technique was used in order to determine the sample respondents.By using simple random sampling technique four sample Kebeles were selected.Cross sectional data were collected from 182 farm households who produced chickpea in 2015/16 production season.Primary data were collected from sample households using structured questionnaire.Descriptive statistics and econometric model were employed to analyze the data.To identify determinants of marketed surplus of chickpea, Ordinary Least Square (OLS) model was employed.The study suggest interventions such as intensification strategies which increase yields through proper management and use of inputs, rural infrastructure improvement increases the likelihood of market orientation and marketed surplus of chickpea.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".