Determinants of the Joint Adoption of Climate-Smart Agriculture Practices by Agro-Pastoralists in Sokoto State, Nigeria
Bibliographic record
Abstract
This study examined the determinants of the joint adoption of climate-smart agriculture practices by agro-pastoralists in Sokoto State, Nigeria. A multi-stage sampling technique was used to select 428 agro-pastoralists who were surveyed using a structured questionnaire. The data were subjected to multivariate probit, ordered probit regression, and factor analysis. The climate-smart practices considered were water, nutrients, carbon, the weather, and crop-smart activities. The results show that the majority of the agro-pastoralists were male (85%), married (90%), and had formal education (55%). The mean score for age, farming experience, household size, and farm size was 44.81 years, 22.26 years, 10.25 persons, and 7.33 hectares, respectively. The multivariate model revealed that land tenure, extension contact, awareness of climate incidences, farming systems, sources of credit, gender, perception, and association membership significantly influenced the joint adoption of climate-smart agricultural practices. This study advocates that resources and conditions that promote the joint adoption of climate-smart practices should be identified to facilitate the dissemination and effective adoption of technologies.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".