Impact of perceptions of climate variability on investment decisions pattern among smallholder rice farmers in Nigeria
Bibliographic record
Abstract
ABSTRACT Although perceptions of climate change have been widely studied, limited attention has been given to how these perceptions influence the investment decisions of smallholder farmers in rice production. This study, therefore, examined the impact of perceived climate variability on investment choices among smallholder rice farmers in the study area. A multi-stage sampling technique was used to select 240 smallholder farmers. Data were collected through field surveys, interviews, and structured questionnaires and were analyzed using descriptive statistics and a Seemingly Unrelated Regression (SUR) model. Descriptive analysis revealed that 61.90% of the rice farmers perceived climate variability in their environment. In response to these perceptions, 86.60% of the farmers invested in labor, 72.80% in herbicides, 66.80% in fertilizers, 46.50% in pesticides, and 34.70% in tractor rentals. Notably, a majority of 58 farmers simultaneously invested in three different inputs. Results from the SUR model indicated that household size, extension services, income, age, farm size, membership in cooperative societies, access to credit, primary occupation, participation in farm associations, years of education, and perception of adverse climatic conditions significantly influenced farmers' investment decisions. The study concludes that smallholder rice farmers tend to make multiple investment decisions as a strategy to cope with climate variability. It recommends that stakeholders involved in climate change mitigation and adaptation initiatives intensify efforts to educate smallholder farmers on the benefits of diversified investment strategies in the face of changing climatic conditions.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".