Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria
Bibliographic record
Abstract
From land cover analysis, cropland expansion was a major driving factor for land use land cover changes in Nigeria from 2000 to 2020. This further highlights the food production needs in the country. While this land use change indicates a significant alteration in land cover, it was exigent to assess land suitability using a Multi-Criteria Decision Analysis (MCDA) combined with geospatial techniques to identify areas with agricultural suitability potential and to analyze cropland suitability. The results showed that the country had 8% of very high suitability, high suitability (25%), moderate suitability (29%), and marginal suitability (25%) croplands. However, low suitability accounts for 14% of the entire cropland. The spatial distribution of cropland suitability shows that most areas in the South East, South South, and South West, respectively, have the most suitable cropland as they meet the biophysical conditions for crop production, followed by the North Central regions, while most places in the North (North East and North West) have a higher share of moderate to low suitability. This study highlights the potential of the country to target localized self-sufficiency. Therefore, this study recommends using the cropland suitability map to launch food security programs across the six geopolitical zones to maximize their inherent environmental potentials to alleviate the country's food production needs.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".