Climate Change and Food Production in Nigeria: Implication for Food Security in Nigeria
Bibliographic record
Abstract
<strong></strong><p>Food production in Nigeria is largely dependent on natural environmental resources like rainfall, temperature and relative humidity. This study examined the impact of some of these climatic variables on food production in Nigeria from 1975 to 2010. Secondary time series data obtained from FAOSTAT (Food and Agriculture Organization statistics) and Nigerian Meteorological Agency were used for the study. The data were analyzed using some econometric tools such as Augmented Dickey Fuller (ADF) test, Johansen Test and Vector Error Correction (VEC) Estimates. The ADF test reveals that relative humidity integrated at order zero level while rainfall and temperature and agricultural output were stationary after differencing at level 1 thus showing that the variables had relationship. The results of the Johansen co-integrated test revealed that there is one co-integrating equation at 5% showing a co-integrating relationship between agricultural output and the climatic variables. The Vector Error Correction Estimates indicated that rainfall was positively significant at 5% to food production on the short run indicating that vagaries in climate especially rainfall affected food production and output in Nigeria. It was recommended, therefore, that measure that could help to mitigate the adverse effects of inadequate rainfall e.g. irrigation, drought resistant crops varieties among others should be put in place by the government. Farmers should equally be sensitized and trained in the area of adaptation and mitigation of the effect of climate change as this will go a long way to ameliorate large scale failure in food production in the country.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".