Impact of Air Pollution on Maize Production in the Sasolburg Area, South Africa
Bibliographic record
Abstract
Previous studies have shown that air pollution has an effect on maize production and can also influence yield. Since the pollution load at Sasolburg exceeds certain air quality standards, it is identified as a ‘hot spot’ and is therefore a perfect region to conduct studies regarding the impact of air pollution on maize production. The Northern Free State region is well known for its high maize production and residents of towns up to 50 km away from Sasolburg indicated that they can smell the fumes of the Sasolburg industries. This study included status of maize production over the past 5 years by visiting farmers every 25 km from Sasolburg, for 100 km. Thus, 4 sampling points of 25 km intervals have been visited. Farmers were asked to fill in questionnaires and provide information related to air pollution and maize production. This was done in three different directions from Sasolburg. It is expected that maize yield near Sasolburg will be less than yield far from Sasolburg if pollution influence maize production. Average maize production by the Free State province as well as individual farmers, and other published and unpublished data which were used, concluded that maize production is in fact, influenced by the air pollution from Sasolburg, low income residents burning coal and other substances in the Sasolburg region. The research also indicated that new technology and techniques make it possible for farmers to maximize maize yield and still, year by year, increase the maize production on their farms. South Africa is able to adapt and work around the air pollution problem, but will enter a downwards maize production curve when the original pollution problem is not addressed. It is therefore recommended that the problem of air pollution in the Sasolburg region be solved, rather than adapting to the circumstances with new agricultural technologies. This study has contributed to maize production, air pollution influences on maize yield and farmer awareness in the Sasolburg region and nearby towns.
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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 | 0.001 |
| 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) | 0.001 | 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".