Maize Production Responsiveness to Land Use Change and Climate Trends in Cameroon
Bibliographic record
Abstract
Most studies on the responsiveness of maize production to various variables have dwelled on the responsiveness of maize production to variations in precipitation or temperature. This study seeks to verify the response of maize production in Cameroon to both climate trends and land use change. Therefore, for the first time, our study presents findings on the relative influence of both climate and land use change on maize production in Cameroon. The data used in this analysis are essentially time series data spanning the period 1961–2006. The data on quantity of maize produced, area of maize harvested and number of maize seeds planted was taken from (http://faostat.fao.org). The mean maize growing season temperature and precipitation data were collected from the 0.5° × 0.5° gridded collaborative datasets of the UNEP and the School of Geography and Environment at Oxford University and from the global crop calendar dataset. The data were analyzed using the average rate of change, detrended simulations, the multiple linear regression technique, correlation coefficient and the coefficient of determination. The results show that maize production in Cameroon is more likely responsive to land use change (forest area change) than rainfall and temperature. However, for the climatic variables, maize production is more responsive to temperature variations than precipitation. In other words, the greater the land use change (forest area loss) the more likely the long run losses in the current maize production gains while rising temperatures were found to be more suitable for maize production. Even though the 1990s marked the period of recovering rainfall levels in most of the Sahel, large fluctuations were still recorded.
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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 | 0.001 |
| 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 | 0.000 |
| 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".