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Record W2053440541 · doi:10.5539/jas.v5n5p76

The Revitalisation of Water Resources for Sustainable Agricultural Development in South Africa: A review

2013· review· en· W2053440541 on OpenAlexvenueno aff
Isaac Azikiwe Agholor

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typereview
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsWater resourcesSustainabilityWater securityBusinessAgricultureNatural resource economicsSustainable developmentCorporate governanceIntegrated water resources managementFood securityWater supplyEquity (law)Environmental planningAgrarian societyEconomic growthWater resource managementEconomicsGeographyPolitical scienceEngineeringEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

The promotion of efficiency and sustainability of water use have been enshrined in the National Water Act (Act 36 of 1998) of South Africa. Currently there is no immediate intractable water crisis facing South Africa, although this is undoubtedly based on the assumption that the existing water resources will be managed effectively. In enhancing revitalisation, the design and supervision of water resources must be linked with the country’s development approach for sustained agrarian reform. It cannot, however, be applauded that the broad social and economic objectives, or even the more specific objective of water security, in South Africa are being fully realized at moment. However, to understand the contemporary issues in revitalisation of water resources for sustainable agricultural development, the paper first describes the various phases of irrigation development in South Africa and highlights the past and present measures taken by the government to ensure equity of access to water. Secondly, it also highlights water security; water demand and supply management; water productivity and water governance in South Africa.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.228
Teacher spread0.206 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2013
Admission routes1
Has abstractyes

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