Trends in Cereal Production and Yield Dynamics in Sub-Saharan Africa Between 1990-2015
Bibliographic record
Abstract
Cereal serves as a very important and vital staple food for many smallholder farming communities in Sub-Saharan Africa (SSA). In this paper, we examined changes in land under cereal production; changes in cereal yield; and changes in cereal production between 1990 and 2015 in Sub-Sahara Africa (SSA). The paper looks at the threats and potential of cereal production with respects to how it helps to address issues of food security and improvements needed to enhance and promote production in the region. The study reveals that 33 (75%) of countries in SSA have experienced an expansion in land under cereal production while 11 (25%) of countries have reduced land under cereal production with an average increase of 679,664 hectares. Further, 32 (73%) of countries have experienced an increase in cereal yield, while 12 (27%) of countries have experienced a reduction in cereal yield, averaging to 311 kg per hectare in SSA. The study also shows that 35 (80%) of countries have experienced an increase in cereal production while 9 (20%) of countries have experienced a reduction in cereal production with a total of 1635201 kg per hectare in SSA. Overall, about 71% of the countries in SSA are experiencing a continuous increase in cereal production, yield levels and land area under cereal production, while about 29% are experiencing a reduction in cereal yield, production levels and land area under cereal production. However, SSA still has the lowest yield growth rate with the highest number of food-insecure persons (35.5% of its population), which is forecast to exacerbate further given the continuous population increase. Hence, it is essential to step up cereal production from the current attainable levels to an actual or reasonable and scalable level through innovative research, training, and technological advancement and production capabilities in the region.
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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 | 0.000 |
| 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".