Commercialization of sorghum milling in Botswana: trends and prospects.
Bibliographic record
Abstract
Commercial sorghum processing in Botswana has grown rapidly during the past decade. The number of sorghum \nmillers has increased four-fold, and sorghum meal has become competitive with maize in urban and rural food \nmarkets. \nIn early 1999, ICRISAT conducted a study of the factors underlying this growth, and the prospects for further \nmarket expansion. The study showed that growth was driven largely by four factors: the traditional consumer \npreference for sorghum meal; strong financial support to millers from the government; the availability of reliable, \nhigh-quality supplies of grain; and effective promot ion of processing technology by a parastatal agency. \nHowever, development of the milling industry had little impact on domestic sorghum production. Productivity in \nBotswana remains too low for the crop to compete with South African imports, and only 2% of the industry's \ngrain purchases are grown domestically. \nKey issues likely to affect future expansion include the identification of alternative sources of grain supplies \n(e.g. Zimbabwe); improvements in product promot ion, market intelligence, and product differentiation (e.g. \ntargeting distinct products for breakfast porridge vs stiff porridge); and the prospects for industry consolidation \ninto a few larger millers. While the Botswana case is not specifically replicable in neighboring countries, the \nstimulus created by linking technology, finance, and raw material supply offers important lessons for the \ndevelopment of commercial crop processing throughout southern Africa.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".