Risk Aversion and Perception of Farmers about Endogenous Risks: An Empirical Study for Maize Producers in Awi Zone, Amhara Region of Ethiopia
Bibliographic record
Abstract
Agriculture is a risky business that is subject to endogenous risks. Endogenous risks caused by input utilization, input affordability and input availability may prove detrimental to the production potential of farmers. The study was aimed at examining the risk perception, risk aversion and risk management strategies of maize producers in Awi zone, which is found in the northwest part of Ethiopia. The study involved 343 respondents who produced maize under risk. Descriptive statistics, a seven-point Likert scale, the observed economic behaviour method, factor analysis and a seemingly unrelated regression model were used to process the data. The results showed that farmers have different perceptions of the endogenous risk associated with input availability and input affordability which has a different probability of occurrence and severity of damage. The observed economic behaviour method showed that farmers in the area also have different risk aversion behaviours: about 7.29% of the respondents in the study area have high risk aversion attitudes, while about 30.61% have medium risk aversion attitudes and 62.10% of them have low risk aversion attitudes. The seemingly unrelated regression model output showed that farmers’ economic, social, demographic and institutional factors, as well as their risk behaviour, determine the risk management strategies that they employ. Maize farmers in the area applied human risk management strategies, production risk management strategies, diversification, financial risk management strategies and marketing risk management strategies to tackle the endogenous risks in the area. It was deduced that maize farmers have a risk averse behaviour even if their risk aversion levels differ based on the scope of the management strategies that they employ to combat risk. Following the finding of the study, a holistic approach to risk management that encompasses all actors, such as farmers, researchers, extension services and financial institutions should be involved to make the appropriate interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".