Profitability drivers of carrot farming and its implications on food security of smallholder farmers in Northwest Ethiopia
Bibliographic record
Abstract
Carrot ( Daucus carota L.) farming plays a dual role in enhancing household nutrition security and generating income for smallholder farmers in Ethiopia. However, the determinants of its profitability and their implications for food security remain insufficiently studied. This study aimed to analyze the key drivers of carrot farming profitability and examine how profitability influences smallholder food security in Northwest Ethiopia. A multi-stage sampling method was used to select three irrigation-accessible districts, followed by random sampling of 385 carrot-producing households. Data were collected using a structured questionnaire covering the 2023–2024 irrigation-based production season, where planting commenced in late November 2023 and harvesting began at the end of March 2024. Descriptive statistics were used to summarize socioeconomic and demographic data related to profitability, and a Log-Log Ordinary Least Squares (OLS) regression model was used to analyze the effects of cost and revenue factors on profitability. Food Consumption Score (FCS) and Meal Frequency were used as food security indicators. The results indicate that while carrot farming is generally profitable, profitability is significantly constrained by high variable costs, particularly costs of labor, seed, irrigation, land preparation, and harvesting, as well as other supplementary expenses. Conversely, profitability was positively and significantly influenced by yield and market price. Importantly, households with above-mean profits reported significantly higher food security outcomes, with a mean FCS of 41.1 and meal frequency of 3.25 meals/day, compared to 22.4 and 2.05 meals/day in less profitable households ( p < 0.001). The study recommends promoting input efficiency, adopting the best agronomic practices, and enhancing market linkages through cooperative models and infrastructure support to improve profitability and its contribution to food security. Integrating livestock for manure and income diversification is also vital. Future longitudinal and cross-regional studies are recommended to address the study’s limitations, which include the regional and seasonal focus, and the lack of household income data for food expenditure.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".