The role of sustainable land management practices in alleviating household food insecurity in Nigeria
Bibliographic record
Abstract
Climate change is a major challenge impacting food security globally. Sub-Saharan African (SSA) countries including Nigeria has experienced the negative effect of climate vagaries most especially on agricultural production, thus, leading to food insecurity. However, sustainable land management (SLM) practices have a huge potential to minimize the impacts on food security in a rapidly changing climate. This study estimates the determinants of the adoption of SLM practices and the impact of adoption on household food security among smallholder rice farmers in Ogun State, Nigeria. A multistage sampling procedure was used to select 120 respondents. A Poisson endogenous treatment (PET) model was employed to analyse the determinants of level of adoption of SLM and impact of SLM adoption on household food security level of smallholder rice farmers in the study area. To account for counterfactuals, a doubly-robust augmented-probability-weighted regression adjustment (APWRA) was also used. In the same vein, the study employed the marginal treatment effects (MTE) approach to estimate the treatment effects heterogeneity. The results showed that socio-economic factors greatly influenced the adoption of SLM practices, such as age and educational level of farmers. The effect of SLM adoption on food security of smallholder farmers was found to be improved when they used SLM package consisting of variety of practices, hence, SLM practices have the potential to alleviate food insecurity among rice farmers if well combined and used to a large extent. The study concluded that knowledge in form of formal education, some form of vocational training, and trainings to access weather information were key to influencing SLM adoption among smallholder farmers in the study area. The treatment effects on untreated (ATU) are lower than that of ATE and ATT, confirming the positive selection on unobserved gains. In particular, the ATU results show that for an average non-adopting household, adoption of SLM practices would significantly improve dietary diversity by about 27%. Farm-level policy efforts that aims to equip farmers through education, trainings and disseminating information on climate change would be a huge step towards the promotion of SLM practice which eventually leads to increased food security. The study recommended that continuous adoption and extensive use can be fostered by encouraging farmers to join a social organisation where related and relevant information on sustainable land management practices is shared through trained agricultural extension officers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".