Analysis of Namibian Main Grain Crops Annual Production, Consumption and Trade—Maize and Pearl Millet
Bibliographic record
Abstract
Cereal grains are the most important source of the world’s total food and staple food for most developing countries. The main objective of this paper is to analyze the Namibian cereal grains by examining trends in annual output, imports and exports as well as consumption volumes for over the period of fifteen years. Due to a variety number of grains being produced and consumed, the main focus is on maize and pearl millet. Data were collected from the Namibian Agronomic Board and from Food and Agriculture Organization of the United Nation Statistical yearbooks for various years. A combination of descriptive statistics has been applied as the method of analysis of the collected data, providing concise summaries about the observations that have been made. The findings show that the production of both maize and pearl millet has increased over the year reviewed due to relative increase in area harvested and yield. Consumption of pearl millet represents one fifth (20%) of the national cereal consumption, while maize represents one third (33%). On average the per capita consumption of maize is around 44kg per year while millet is about 29kg per year. The consumption of both maize and pearl millet rose at an average annual rate higher than the production rate, particularly for maize. To cover deficits between consumption and production, imports become a viable option, especially for maize.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".