Rainfall Variability and Food Crop Vulnerability in Ndu Sub-Division, North West Region of Cameroon
Bibliographic record
Abstract
Little scientific evidence exists in the context of climate variability and food crop production in Ndu. This study seeks to assess the impact of rainfall variability on food crop vulnerability in Ndu Sub-Division. The primary data were gotten through field surveys. A total of 200 farmers were sampled and questionnaires were administered to them. Descriptive and inferential statistical techniques were employed to analyze the data. Results were presented in tables and climographs. Formulated hypotheses were tested using the least square regression model to establish the extent of exposure and sensitivity of rainfall variability on food crop production. The Pearson Product Moment Correlation Coefficient was used to describe the trends of variations in rainfall. Statistically, rainfall accounted for 19.5% of variability in maize production while 50.87% accounted for variability in beans production. Furthermore, 30.1% accounted for variations in potatoes production. From these statistics it was then concluded that rainfall variability minimally affects maize and beans but had a significant effect on maize production in Ndu. The research study also revealed that rainfall shows a decreasing trend. The study recommended, amongst others the need for farmers to adopt more sustainable agricultural practices and the increased use of more resistant crop species that can withstand exposure and sensitivity to rainfall variability. The study concluded that a bottom-up approach should be employed in order to improve on the adaptive capacities of the agricultural sector in Ndu.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".