Effects of Rainfall and Temperature Oscillations on Maize Yields in Buea Sub-Division, Cameroon
Bibliographic record
Abstract
There has been increasing concerns about the continuous variability in the climatic parameters of rainfall and temperature due to their manifold impacts. Some of these effects are observed through changes in crop yields such as maize in most parts of Sub-Saharan Africa which lacks the capital and technological viabilities to deal with the situation. This paper therefore examines the effects of the growing climatic oscillations on maize production. The study used primary and secondary data in order to provide insights on the quantitative effects of rainfall and temperature on maize yields in Buea Sub-Division. The climatic and crop trend analyses were done using simple regressions, means, and standard deviations. These were done using 2010 Excel Software. The impacts of varying rainfall and temperature on maize yields were determined using the logistic regression analysis in Stata 10 statistical software. Based on the analysis, results show that there has been growing rainfall and temperature fluctuations over Buea. This has been x-rayed through the increasing temperatures, slight declines in rainfall amounts, and the unpredictability of the sensation and the departure of the rains. Other effects have been observed through short dry spells especially in the months of April as well as increasing flooding of some farmlands in the months of August and September. Results further show that the unpredictability of the commencement of the rains has shifted the sowing season of maize by an average of four weeks and because of this situation, maize yields have increased during the minor season more than yields in the main season. Other climatic impacts were observed through increasing maize attacks from pests and diseases. As the way forward, there is the need for the development of maize germplasms that are heat-tolerant and need for the concentration of maize in the second season when soil moisture to ensure maize seed germination, growth and maturity is assured.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".