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Record W2005780746 · doi:10.5539/jas.v5n11p181

Impact of Air Pollution on Maize Production in the Sasolburg Area, South Africa

2013· article· en· W2005780746 on OpenAlexvenueno aff
Phokele Maponya, Isaac Tebogo Rampedi

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
Fundersnot available
KeywordsPollutionProduction (economics)Yield (engineering)Environmental scienceAir pollutionAir quality indexGeographyEnvironmental protectionAgronomyAgricultural economicsMeteorologyBiologyEcologyEconomics

Abstract

fetched live from OpenAlex

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 Average maize production by the Free State province as well as individual farmers, and other published and unpublished data which were used, concluded that maize production is in fact, influenced by the air pollution from Sasolburg, low income residents burning coal and other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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.222
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2013
Admission routes1
Has abstractyes

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