Does Adoption of Climate Change Adaptation Strategy Improve Food Security? A Case of Rice Farmers in Ogun State, Nigeria
Bibliographic record
Abstract
The southwestern part of Nigeria, particularly Ogun State, is more vulnerable to the vagaries of climate change due to the high dependence on rain-fed agriculture and limited capacities to respond to climate change. In this study, factors influencing climate change adaptation strategies and its impacts on household food security of smallholder rice farmers in Ogun State were estimated. A multistage sampling technique was employed to select 120 smallholder rice farmers in the study area. The factors influencing the adoption of climate change adaptation practices and their impacts on household food security among smallholder rice farmers in Ogun State were examined using a probit model and an endogenous switching probit model (ESPM). According to the results of household dietary diversity score (HDDS), adopters of climate change adaptation techniques have higher levels of food security than non-adopters. The outcome of the ESPM shows that access to market information, access to extension agents, gender, off-farm income, and membership in cooperatives all contribute to the variations in food security experienced by both adopters and non-adopters of climate change adaptation strategies. A unit increase in adoption of climate change adaptation measures will increase household food security by about 3 units while decreasing severity in food insecurity by about 3.2 units. Therefore, it is recommended that policies that would support smallholder farmers’ decisions to embrace measures for coping with climate change should be encouraged in order to stimulate their adaptive capacity. Additionally, in order to secure the inclusive sustainability of the agricultural sector, stakeholders and NGOs must collaborate with each other to enhance the circumstances under which farmers may receive climate change information, timely agricultural loans, and policy incentives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".