Analysis of Horticultural Production Trends in Botswana
Bibliographic record
Abstract
An analysis of vegetable and fruit production trends in Botswana was carried out focussing on 1997 to 2009 cropping years. For vegetable production the results showed an increase in yield and total production from 1997 to 2009. The area planted with vegetables accounted for only 3% of variation in yield indicating that the area planted with vegetables had no significant effect on yield during that period. The national demands for vegetables increased from 1997 to 2009, but imports started to decline in 2001. Regression analysis showed a significant decline in imports as total production increased. Total production accounted for more than 50% of variation in imports indicating that a significant proportion of national demand was met by local production. Total fruit production increased from 3000 to over 9000 tons from 2003 to 2008 but declined in 2009. Yield increased twofold from 2003 to 2005 but declined by more than 50% at the end of 2009. Regression analysis showed a significant decrease in yield as area planted increased; indicating a decrease in productivity. The national fruit production was surpassed by demand during 2003 to 2009 revealing a deficit in fruit production. The total imports of fruits declined significantly as total production increased suggesting that some of the demands for fruits were catered for by local production. These results suggest that famers need to be trained on good management of crops so that productivity can increase with increase in area planted to horticultural crops.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".